M.L. van de Ruit
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17 records found
1
Reconstructing Neural Implant Geometry Using Subthreshold Stimulation
Towards Autonomous Phosphene Mapping
Visual prostheses aim to restore functional visual perception in individuals who are blind. Phosphene mapping is essential for these devices, as it establishes the relationship between implanted electrodes and locations in the visual field. Current approaches rely heavily on patient feedback, making the mapping process time-consuming and difficult to scale to high-density electrode arrays.
This thesis investigates Subthreshold Electrode Mapping (SEM) as a framework for automatically reconstructing the relative geometry of an implanted electrode array using subthreshold electrical stimulation. In SEM, stimulation signals recorded across the array are used to estimate inter-electrode distances, which are subsequently used to reconstruct the relative electrode positions. These positions can then be combined with a retinotopic model to support the phosphene mapping process.
The performance of different amplitude estimation approaches was investigated using analytical derivations and Monte Carlo simulations under several synthetic noise conditions. A frequency-domain approach consistently provided the most accurate amplitude estimates under the simulated conditions. The resulting distance estimates were incorporated into a precision-weighted geometry reconstruction framework based on multidimensional scaling and weighted SMACOF. Simulation results demonstrated that SEM can reconstruct the relative electrode geometry with high accuracy and that reconstruction performance improves with increasing stimulation budget.
Overall, the results demonstrate the feasibility of SEM as a potential approach for relative electrode localization and provide a simulation framework for evaluating its performance. Further experimental validation is required to assess its performance under realistic neural tissue and noise conditions. ...
This thesis investigates Subthreshold Electrode Mapping (SEM) as a framework for automatically reconstructing the relative geometry of an implanted electrode array using subthreshold electrical stimulation. In SEM, stimulation signals recorded across the array are used to estimate inter-electrode distances, which are subsequently used to reconstruct the relative electrode positions. These positions can then be combined with a retinotopic model to support the phosphene mapping process.
The performance of different amplitude estimation approaches was investigated using analytical derivations and Monte Carlo simulations under several synthetic noise conditions. A frequency-domain approach consistently provided the most accurate amplitude estimates under the simulated conditions. The resulting distance estimates were incorporated into a precision-weighted geometry reconstruction framework based on multidimensional scaling and weighted SMACOF. Simulation results demonstrated that SEM can reconstruct the relative electrode geometry with high accuracy and that reconstruction performance improves with increasing stimulation budget.
Overall, the results demonstrate the feasibility of SEM as a potential approach for relative electrode localization and provide a simulation framework for evaluating its performance. Further experimental validation is required to assess its performance under realistic neural tissue and noise conditions. ...
Visual prostheses aim to restore functional visual perception in individuals who are blind. Phosphene mapping is essential for these devices, as it establishes the relationship between implanted electrodes and locations in the visual field. Current approaches rely heavily on patient feedback, making the mapping process time-consuming and difficult to scale to high-density electrode arrays.
This thesis investigates Subthreshold Electrode Mapping (SEM) as a framework for automatically reconstructing the relative geometry of an implanted electrode array using subthreshold electrical stimulation. In SEM, stimulation signals recorded across the array are used to estimate inter-electrode distances, which are subsequently used to reconstruct the relative electrode positions. These positions can then be combined with a retinotopic model to support the phosphene mapping process.
The performance of different amplitude estimation approaches was investigated using analytical derivations and Monte Carlo simulations under several synthetic noise conditions. A frequency-domain approach consistently provided the most accurate amplitude estimates under the simulated conditions. The resulting distance estimates were incorporated into a precision-weighted geometry reconstruction framework based on multidimensional scaling and weighted SMACOF. Simulation results demonstrated that SEM can reconstruct the relative electrode geometry with high accuracy and that reconstruction performance improves with increasing stimulation budget.
Overall, the results demonstrate the feasibility of SEM as a potential approach for relative electrode localization and provide a simulation framework for evaluating its performance. Further experimental validation is required to assess its performance under realistic neural tissue and noise conditions.
This thesis investigates Subthreshold Electrode Mapping (SEM) as a framework for automatically reconstructing the relative geometry of an implanted electrode array using subthreshold electrical stimulation. In SEM, stimulation signals recorded across the array are used to estimate inter-electrode distances, which are subsequently used to reconstruct the relative electrode positions. These positions can then be combined with a retinotopic model to support the phosphene mapping process.
The performance of different amplitude estimation approaches was investigated using analytical derivations and Monte Carlo simulations under several synthetic noise conditions. A frequency-domain approach consistently provided the most accurate amplitude estimates under the simulated conditions. The resulting distance estimates were incorporated into a precision-weighted geometry reconstruction framework based on multidimensional scaling and weighted SMACOF. Simulation results demonstrated that SEM can reconstruct the relative electrode geometry with high accuracy and that reconstruction performance improves with increasing stimulation budget.
Overall, the results demonstrate the feasibility of SEM as a potential approach for relative electrode localization and provide a simulation framework for evaluating its performance. Further experimental validation is required to assess its performance under realistic neural tissue and noise conditions.
Redeveloped scissor lift
Using an adjustable statically balanced spring mechanism
This thesis will improve a compact lifting aid that was developed in a previous thesis, which combined a scissor lifting mechanism with static balancing technology to ensure intuitive use. The main improvement points that were taken from this previous work were Spring mechanism compactness, Dimensioned load scalability, Extended frame stability, and Intuitive braking reliability. After formulating design requirements for each of these sub-functionalities, concepts were generated, evaluated, and combined into a final design. Through theoretical calculations, a full 3D CAD design, and a physical demonstrator, this new design is then assessed to meet the initial design requirements. The new design meets the compactness requirements, as the full device fits within a footprint of 0.4 x 0.2 meters. It shows improved stability, as at the maximal reach of 0.8 meters it hardly moves laterally under representative load. The custom made linear guidance system is more robust than the previous model, without increasing unwanted friction. And finally, the braking system now allows for safe blocking of the mechanism at each desired height rather than at set intervals. After these requirement tests, a more general evaluation of the project is performed, linking the current work to the problem it aimed to solve by assessing user requirements and proposing directions for future work.
...
This thesis will improve a compact lifting aid that was developed in a previous thesis, which combined a scissor lifting mechanism with static balancing technology to ensure intuitive use. The main improvement points that were taken from this previous work were Spring mechanism compactness, Dimensioned load scalability, Extended frame stability, and Intuitive braking reliability. After formulating design requirements for each of these sub-functionalities, concepts were generated, evaluated, and combined into a final design. Through theoretical calculations, a full 3D CAD design, and a physical demonstrator, this new design is then assessed to meet the initial design requirements. The new design meets the compactness requirements, as the full device fits within a footprint of 0.4 x 0.2 meters. It shows improved stability, as at the maximal reach of 0.8 meters it hardly moves laterally under representative load. The custom made linear guidance system is more robust than the previous model, without increasing unwanted friction. And finally, the braking system now allows for safe blocking of the mechanism at each desired height rather than at set intervals. After these requirement tests, a more general evaluation of the project is performed, linking the current work to the problem it aimed to solve by assessing user requirements and proposing directions for future work.
Muscle spindles sense and encode changes in muscle length into the firing of their primary afferents and are often considered to offer the most important contribution to proprioception. Understanding the mechanosensory transduction of the spindle’s primary afferents is therefore critical to our understanding of sensorimotor control. Computational muscle spindle models offer a non-invasive framework to simulate and predict their sensory firing. Most spindle models, however, are phenomenological models that generalise poorly to conditions for which they were not optimised. In a more recent, biophysical approach by Blum et al. (2020), characteristic Ia afferent firing properties emerge from intrafusal contractile mechanics and its interaction with the muscle and tendon. The goal of this study was to assess whether the model’s biophysical realism improves the simulation of the human short- (M1) and long-latency (M2) stretch reflex, as observed in EMG, compared to a phenomenological model, and, more broadly, whether biophysically grounded spindle models offer a worthwhile foundation for sensorimotor modelling. The biophysical model was first fitted to human afferent firing data. EMG-like signals were then simulated by driving a motoneuron pool model with the Ia afferent response of each spindle model to a set of fast ramp-and-hold trajectories and subsequently compared to experimentally obtained EMG signals under the same stretch perturbations. The dependencies of the simulated M1 and M2 responses on various ramp properties (ramp velocity, amplitude, onset acceleration and duration) were compared to those observed experimentally. The biophysical model reproduced the characteristic initial burst and acceleration-dependent M1 scaling, which the phenomenological model could not. However, the biophysical model consistently overshot the known bounds of physiological firing rates and produced exaggerated reflex responses owing to scarce microneurography data for fitting. Neither model could reproduce the full experimental M2 response and its dependencies, as both models were structurally incapable of capturing its full spinal and transcortical origin. The mechanistic basis of the biophysical model nonetheless makes it a promising foundation for sensorimotor modelling, provided it is constrained by more extensive human data.
...
Muscle spindles sense and encode changes in muscle length into the firing of their primary afferents and are often considered to offer the most important contribution to proprioception. Understanding the mechanosensory transduction of the spindle’s primary afferents is therefore critical to our understanding of sensorimotor control. Computational muscle spindle models offer a non-invasive framework to simulate and predict their sensory firing. Most spindle models, however, are phenomenological models that generalise poorly to conditions for which they were not optimised. In a more recent, biophysical approach by Blum et al. (2020), characteristic Ia afferent firing properties emerge from intrafusal contractile mechanics and its interaction with the muscle and tendon. The goal of this study was to assess whether the model’s biophysical realism improves the simulation of the human short- (M1) and long-latency (M2) stretch reflex, as observed in EMG, compared to a phenomenological model, and, more broadly, whether biophysically grounded spindle models offer a worthwhile foundation for sensorimotor modelling. The biophysical model was first fitted to human afferent firing data. EMG-like signals were then simulated by driving a motoneuron pool model with the Ia afferent response of each spindle model to a set of fast ramp-and-hold trajectories and subsequently compared to experimentally obtained EMG signals under the same stretch perturbations. The dependencies of the simulated M1 and M2 responses on various ramp properties (ramp velocity, amplitude, onset acceleration and duration) were compared to those observed experimentally. The biophysical model reproduced the characteristic initial burst and acceleration-dependent M1 scaling, which the phenomenological model could not. However, the biophysical model consistently overshot the known bounds of physiological firing rates and produced exaggerated reflex responses owing to scarce microneurography data for fitting. Neither model could reproduce the full experimental M2 response and its dependencies, as both models were structurally incapable of capturing its full spinal and transcortical origin. The mechanistic basis of the biophysical model nonetheless makes it a promising foundation for sensorimotor modelling, provided it is constrained by more extensive human data.
Parametric joint impedance models with physiological parameters can provide interpretable measures of neuromechanical function that can be translated to clinically described phenomena. However, reliable identification of these parameters is challenging due to measurement noise, inter-trial variability of humans, and the absence of a ground-truth model. Hence, this work explores the possibility of applying regularization in the form of Bayesian inference for improved physiological parameter fits. Experimental data of the wrist (torque and rotation angle) was collected from 41 healthy adult participants using a haptic wrist manipulator. This data was used to estimate the parameters of a second order mass–spring–damper wrist model. Within the time-domain the instrumental variable (IV) technique combined with linear least squares (IV+LLS) was compared with a combination of IV and Bayesian inference (IV+Bayes) for the identification of the model parameters. The prior distributions for the implementation of Bayesian inference where assumed Gaussian. The means and variances of these prior distributions were based upon the model parameters of all of the participants identified through the IV+LLS method. The quality of the fitted parameters was evaluated using time-domain simulation with the IV+LLS estimated parameters and the maximum a posteriori (MAP) IV+Bayes estimated parameters. Quantification of the quality of the parameter fits was done using the variance-accounted-for (VAF) metric. An average VAF of 0.53 was reported for the IV approach and an average VAF of 0.52 for the combined IV and Bayesian inference approach. A paired-sample t-test showed no significant difference between the estimated VAF values resulting from the different identification methods. The results indicate that the incorporation of Bayesian inference within closed loop system identification in the time domain is feasible. On average the IV+Bayes method demonstrated no significantly different VAF values compared to the IV+LLS method. Although for individual cases these differences varied widely. Future work could explore the impact of different prior assumptions, data properties, and model complexity and could explore the possibility of implementing Bayesian inference within system identification in the frequency domain. This could provide insights into the situations in which the Bayesian approach would proof valuable for the assessment of physiological meaningful parameters.
...
Parametric joint impedance models with physiological parameters can provide interpretable measures of neuromechanical function that can be translated to clinically described phenomena. However, reliable identification of these parameters is challenging due to measurement noise, inter-trial variability of humans, and the absence of a ground-truth model. Hence, this work explores the possibility of applying regularization in the form of Bayesian inference for improved physiological parameter fits. Experimental data of the wrist (torque and rotation angle) was collected from 41 healthy adult participants using a haptic wrist manipulator. This data was used to estimate the parameters of a second order mass–spring–damper wrist model. Within the time-domain the instrumental variable (IV) technique combined with linear least squares (IV+LLS) was compared with a combination of IV and Bayesian inference (IV+Bayes) for the identification of the model parameters. The prior distributions for the implementation of Bayesian inference where assumed Gaussian. The means and variances of these prior distributions were based upon the model parameters of all of the participants identified through the IV+LLS method. The quality of the fitted parameters was evaluated using time-domain simulation with the IV+LLS estimated parameters and the maximum a posteriori (MAP) IV+Bayes estimated parameters. Quantification of the quality of the parameter fits was done using the variance-accounted-for (VAF) metric. An average VAF of 0.53 was reported for the IV approach and an average VAF of 0.52 for the combined IV and Bayesian inference approach. A paired-sample t-test showed no significant difference between the estimated VAF values resulting from the different identification methods. The results indicate that the incorporation of Bayesian inference within closed loop system identification in the time domain is feasible. On average the IV+Bayes method demonstrated no significantly different VAF values compared to the IV+LLS method. Although for individual cases these differences varied widely. Future work could explore the impact of different prior assumptions, data properties, and model complexity and could explore the possibility of implementing Bayesian inference within system identification in the frequency domain. This could provide insights into the situations in which the Bayesian approach would proof valuable for the assessment of physiological meaningful parameters.
Master thesis
(2026)
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I. Goes, Rogier C.J. De Jonge, A.C. Schouten, Jan Willem Kuiper, Eris van Twist, Brian van Winden, M.L. van de Ruit
Introduction: Cerebral autoregulation (CA) maintains stable cerebral blood flow, and its impairment is common in conditions such as traumatic brain injury. Early detection is crucial for guiding therapy and improving patient outcomes. Conventional correlation-based indices, including the pressure reactivity index (PRx) and cerebral oximetry index (COx), assume linearity and signal stationarity, which may not capture the dynamic nature of CA. Wavelet-based methods, incorporating both time and frequency domains, may provide a more robust assessment.
Objectives: This thesis aimed to develop a wavelet-based approach using mean arterial pressure (MAP) and near-infrared spectroscopy (NIRS)-based cerebral regional oxygen saturation (CrSO2) to assess CA in neonates undergoing surgery for congenital diaphragmatic hernia (CDH) or oesophageal atresia (EA).
Methods: The data used for this study consisted of CDH and EA patients who underwent surgery at the Erasmus MC Sophia Children's Hospital in Rotterdam between July 2018 and July 2020. A retrospective analysis of MAP and the mean of left and right CrSO2 signals across pre-, intra-, and postoperative phases was performed to assess CA using wavelet-based methods. To distinguish true peaks from random fluctuations in the wavelet coherence spectrum, a significance threshold was established via Monte Carlo simulations. Wavelet coherence was calculated, after which the threshold was applied to determine the percentage of significant (in-phase) coherence across frequencies, the wavelet-based cerebral oximetry index (wCOx), and the median coherence and semblance within low (0.0067–0.015 Hz), mid (0.015–0.03 Hz), and high (0.03–0.05 Hz) frequency bands. The wCOx and COx were visually compared.
Results: Twenty patients with CDH and four with EA were included. Wavelet coherence, in-phase coherence, and semblance increased intraoperatively, with the percentage of significant coherence peaking around 0.01 Hz. These wavelet-derived metrics revealed frequency-dependent changes and phase relationships that may reflect intraoperative variation in CA influenced by anaesthesia-related physiological and pharmacological factors. However, the wCOx, calculated using 500 s and 800 s time windows, was limited by consistently low coherence, and the absence of a definitive gold standard for CA further constrained interpretation.
Conclusion: This study demonstrated the potential of wavelet-based metrics, including coherence, in-phase coherence, and semblance, for assessing CA in a neonatal surgical population. Although the wCOx was not consistently applicable, the multiple wavelet metrics provided valuable insight into perioperative changes and signal dynamics beyond conventional correlation-based measures. These findings highlight the need for further validation in larger cohorts with cerebral illness to establish clinical relevance and explore integration with patient outcomes and optimal MAP assessment. ...
Objectives: This thesis aimed to develop a wavelet-based approach using mean arterial pressure (MAP) and near-infrared spectroscopy (NIRS)-based cerebral regional oxygen saturation (CrSO2) to assess CA in neonates undergoing surgery for congenital diaphragmatic hernia (CDH) or oesophageal atresia (EA).
Methods: The data used for this study consisted of CDH and EA patients who underwent surgery at the Erasmus MC Sophia Children's Hospital in Rotterdam between July 2018 and July 2020. A retrospective analysis of MAP and the mean of left and right CrSO2 signals across pre-, intra-, and postoperative phases was performed to assess CA using wavelet-based methods. To distinguish true peaks from random fluctuations in the wavelet coherence spectrum, a significance threshold was established via Monte Carlo simulations. Wavelet coherence was calculated, after which the threshold was applied to determine the percentage of significant (in-phase) coherence across frequencies, the wavelet-based cerebral oximetry index (wCOx), and the median coherence and semblance within low (0.0067–0.015 Hz), mid (0.015–0.03 Hz), and high (0.03–0.05 Hz) frequency bands. The wCOx and COx were visually compared.
Results: Twenty patients with CDH and four with EA were included. Wavelet coherence, in-phase coherence, and semblance increased intraoperatively, with the percentage of significant coherence peaking around 0.01 Hz. These wavelet-derived metrics revealed frequency-dependent changes and phase relationships that may reflect intraoperative variation in CA influenced by anaesthesia-related physiological and pharmacological factors. However, the wCOx, calculated using 500 s and 800 s time windows, was limited by consistently low coherence, and the absence of a definitive gold standard for CA further constrained interpretation.
Conclusion: This study demonstrated the potential of wavelet-based metrics, including coherence, in-phase coherence, and semblance, for assessing CA in a neonatal surgical population. Although the wCOx was not consistently applicable, the multiple wavelet metrics provided valuable insight into perioperative changes and signal dynamics beyond conventional correlation-based measures. These findings highlight the need for further validation in larger cohorts with cerebral illness to establish clinical relevance and explore integration with patient outcomes and optimal MAP assessment. ...
Introduction: Cerebral autoregulation (CA) maintains stable cerebral blood flow, and its impairment is common in conditions such as traumatic brain injury. Early detection is crucial for guiding therapy and improving patient outcomes. Conventional correlation-based indices, including the pressure reactivity index (PRx) and cerebral oximetry index (COx), assume linearity and signal stationarity, which may not capture the dynamic nature of CA. Wavelet-based methods, incorporating both time and frequency domains, may provide a more robust assessment.
Objectives: This thesis aimed to develop a wavelet-based approach using mean arterial pressure (MAP) and near-infrared spectroscopy (NIRS)-based cerebral regional oxygen saturation (CrSO2) to assess CA in neonates undergoing surgery for congenital diaphragmatic hernia (CDH) or oesophageal atresia (EA).
Methods: The data used for this study consisted of CDH and EA patients who underwent surgery at the Erasmus MC Sophia Children's Hospital in Rotterdam between July 2018 and July 2020. A retrospective analysis of MAP and the mean of left and right CrSO2 signals across pre-, intra-, and postoperative phases was performed to assess CA using wavelet-based methods. To distinguish true peaks from random fluctuations in the wavelet coherence spectrum, a significance threshold was established via Monte Carlo simulations. Wavelet coherence was calculated, after which the threshold was applied to determine the percentage of significant (in-phase) coherence across frequencies, the wavelet-based cerebral oximetry index (wCOx), and the median coherence and semblance within low (0.0067–0.015 Hz), mid (0.015–0.03 Hz), and high (0.03–0.05 Hz) frequency bands. The wCOx and COx were visually compared.
Results: Twenty patients with CDH and four with EA were included. Wavelet coherence, in-phase coherence, and semblance increased intraoperatively, with the percentage of significant coherence peaking around 0.01 Hz. These wavelet-derived metrics revealed frequency-dependent changes and phase relationships that may reflect intraoperative variation in CA influenced by anaesthesia-related physiological and pharmacological factors. However, the wCOx, calculated using 500 s and 800 s time windows, was limited by consistently low coherence, and the absence of a definitive gold standard for CA further constrained interpretation.
Conclusion: This study demonstrated the potential of wavelet-based metrics, including coherence, in-phase coherence, and semblance, for assessing CA in a neonatal surgical population. Although the wCOx was not consistently applicable, the multiple wavelet metrics provided valuable insight into perioperative changes and signal dynamics beyond conventional correlation-based measures. These findings highlight the need for further validation in larger cohorts with cerebral illness to establish clinical relevance and explore integration with patient outcomes and optimal MAP assessment.
Objectives: This thesis aimed to develop a wavelet-based approach using mean arterial pressure (MAP) and near-infrared spectroscopy (NIRS)-based cerebral regional oxygen saturation (CrSO2) to assess CA in neonates undergoing surgery for congenital diaphragmatic hernia (CDH) or oesophageal atresia (EA).
Methods: The data used for this study consisted of CDH and EA patients who underwent surgery at the Erasmus MC Sophia Children's Hospital in Rotterdam between July 2018 and July 2020. A retrospective analysis of MAP and the mean of left and right CrSO2 signals across pre-, intra-, and postoperative phases was performed to assess CA using wavelet-based methods. To distinguish true peaks from random fluctuations in the wavelet coherence spectrum, a significance threshold was established via Monte Carlo simulations. Wavelet coherence was calculated, after which the threshold was applied to determine the percentage of significant (in-phase) coherence across frequencies, the wavelet-based cerebral oximetry index (wCOx), and the median coherence and semblance within low (0.0067–0.015 Hz), mid (0.015–0.03 Hz), and high (0.03–0.05 Hz) frequency bands. The wCOx and COx were visually compared.
Results: Twenty patients with CDH and four with EA were included. Wavelet coherence, in-phase coherence, and semblance increased intraoperatively, with the percentage of significant coherence peaking around 0.01 Hz. These wavelet-derived metrics revealed frequency-dependent changes and phase relationships that may reflect intraoperative variation in CA influenced by anaesthesia-related physiological and pharmacological factors. However, the wCOx, calculated using 500 s and 800 s time windows, was limited by consistently low coherence, and the absence of a definitive gold standard for CA further constrained interpretation.
Conclusion: This study demonstrated the potential of wavelet-based metrics, including coherence, in-phase coherence, and semblance, for assessing CA in a neonatal surgical population. Although the wCOx was not consistently applicable, the multiple wavelet metrics provided valuable insight into perioperative changes and signal dynamics beyond conventional correlation-based measures. These findings highlight the need for further validation in larger cohorts with cerebral illness to establish clinical relevance and explore integration with patient outcomes and optimal MAP assessment.
Master thesis
(2025)
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J.T. Kok, K.J.P. Jongbloed, E. van der Kruk, F.C.T. van der Helm, A.K. Silverman, M.L. van de Ruit
Background: Anterior cruciate ligament (ACL) injuries commonly reduce knee stability and increase joint loading, often leading to compensatory gait alterations that may increase injury risk. Functional electrical stimulation (FES) of the biceps femoris long head (BFLH) during the gait stance may improve knee stability by reducing harmful joint loading, but the effects on voluntary muscle control remain unclear.
Research question: This study examined whether FES of the BFLH during the stance phase of the gait reduces ACL-relevant knee joint loading in healthy adults and whether it alters voluntary muscle control. Additionally, the use of gluteus maximus (GLMAX) sEMG as a proxy for BFLH activation was assessed.
Method: Nine healthy participants walked on a treadmill under control and FES-assisted conditions. Kinematic, kinetic, and sEMG data were analyzed using statistical parametric mapping and linear mixed-effects models.
Results: FES of the BFLH significantly reduced internal knee rotation moment (KRM) with 9.37% during 42–48% of the gait cycle (p = 0.0002; d = 0.42). Knee adduction moment (KAM) showed non-significant reductions in both legs (non-stimulated: p = 0.0317, d = 0.18; stimulated: p = 0.0492, d = 0.37). Knee abduction angle (KAA) and knee rotation angle (KRA) showed no significant changes (p > 0.05). In sEMG analysis, inconsistent timing between GLMAX and BFLH activation indicated GLMAX is not a reliable surrogate for estimating BFLH activity. Regarding voluntary control, only peak KAM increased slightly over strides during FES-assisted walking (p = 0.006), possibly due to muscle fatigue. No significant retention or after-effects were observed.
Conclusion: Targeted FES of the BFLH can reduce ACL-relevant knee loading without impairing voluntary motor control. sEMG results highlight the need for direct BFLH monitoring, as GLMAX is an unreliable proxy. These findings support further exploration of FES strategies for ACL injury prevention and rehabilitation.
...
Research question: This study examined whether FES of the BFLH during the stance phase of the gait reduces ACL-relevant knee joint loading in healthy adults and whether it alters voluntary muscle control. Additionally, the use of gluteus maximus (GLMAX) sEMG as a proxy for BFLH activation was assessed.
Method: Nine healthy participants walked on a treadmill under control and FES-assisted conditions. Kinematic, kinetic, and sEMG data were analyzed using statistical parametric mapping and linear mixed-effects models.
Results: FES of the BFLH significantly reduced internal knee rotation moment (KRM) with 9.37% during 42–48% of the gait cycle (p = 0.0002; d = 0.42). Knee adduction moment (KAM) showed non-significant reductions in both legs (non-stimulated: p = 0.0317, d = 0.18; stimulated: p = 0.0492, d = 0.37). Knee abduction angle (KAA) and knee rotation angle (KRA) showed no significant changes (p > 0.05). In sEMG analysis, inconsistent timing between GLMAX and BFLH activation indicated GLMAX is not a reliable surrogate for estimating BFLH activity. Regarding voluntary control, only peak KAM increased slightly over strides during FES-assisted walking (p = 0.006), possibly due to muscle fatigue. No significant retention or after-effects were observed.
Conclusion: Targeted FES of the BFLH can reduce ACL-relevant knee loading without impairing voluntary motor control. sEMG results highlight the need for direct BFLH monitoring, as GLMAX is an unreliable proxy. These findings support further exploration of FES strategies for ACL injury prevention and rehabilitation.
...
Background: Anterior cruciate ligament (ACL) injuries commonly reduce knee stability and increase joint loading, often leading to compensatory gait alterations that may increase injury risk. Functional electrical stimulation (FES) of the biceps femoris long head (BFLH) during the gait stance may improve knee stability by reducing harmful joint loading, but the effects on voluntary muscle control remain unclear.
Research question: This study examined whether FES of the BFLH during the stance phase of the gait reduces ACL-relevant knee joint loading in healthy adults and whether it alters voluntary muscle control. Additionally, the use of gluteus maximus (GLMAX) sEMG as a proxy for BFLH activation was assessed.
Method: Nine healthy participants walked on a treadmill under control and FES-assisted conditions. Kinematic, kinetic, and sEMG data were analyzed using statistical parametric mapping and linear mixed-effects models.
Results: FES of the BFLH significantly reduced internal knee rotation moment (KRM) with 9.37% during 42–48% of the gait cycle (p = 0.0002; d = 0.42). Knee adduction moment (KAM) showed non-significant reductions in both legs (non-stimulated: p = 0.0317, d = 0.18; stimulated: p = 0.0492, d = 0.37). Knee abduction angle (KAA) and knee rotation angle (KRA) showed no significant changes (p > 0.05). In sEMG analysis, inconsistent timing between GLMAX and BFLH activation indicated GLMAX is not a reliable surrogate for estimating BFLH activity. Regarding voluntary control, only peak KAM increased slightly over strides during FES-assisted walking (p = 0.006), possibly due to muscle fatigue. No significant retention or after-effects were observed.
Conclusion: Targeted FES of the BFLH can reduce ACL-relevant knee loading without impairing voluntary motor control. sEMG results highlight the need for direct BFLH monitoring, as GLMAX is an unreliable proxy. These findings support further exploration of FES strategies for ACL injury prevention and rehabilitation.
Research question: This study examined whether FES of the BFLH during the stance phase of the gait reduces ACL-relevant knee joint loading in healthy adults and whether it alters voluntary muscle control. Additionally, the use of gluteus maximus (GLMAX) sEMG as a proxy for BFLH activation was assessed.
Method: Nine healthy participants walked on a treadmill under control and FES-assisted conditions. Kinematic, kinetic, and sEMG data were analyzed using statistical parametric mapping and linear mixed-effects models.
Results: FES of the BFLH significantly reduced internal knee rotation moment (KRM) with 9.37% during 42–48% of the gait cycle (p = 0.0002; d = 0.42). Knee adduction moment (KAM) showed non-significant reductions in both legs (non-stimulated: p = 0.0317, d = 0.18; stimulated: p = 0.0492, d = 0.37). Knee abduction angle (KAA) and knee rotation angle (KRA) showed no significant changes (p > 0.05). In sEMG analysis, inconsistent timing between GLMAX and BFLH activation indicated GLMAX is not a reliable surrogate for estimating BFLH activity. Regarding voluntary control, only peak KAM increased slightly over strides during FES-assisted walking (p = 0.006), possibly due to muscle fatigue. No significant retention or after-effects were observed.
Conclusion: Targeted FES of the BFLH can reduce ACL-relevant knee loading without impairing voluntary motor control. sEMG results highlight the need for direct BFLH monitoring, as GLMAX is an unreliable proxy. These findings support further exploration of FES strategies for ACL injury prevention and rehabilitation.
Hydrofoil vessels show unstable behaviour when operating in foilborne condition. Therefore, an active control system (ACS) is used. Designing an ACS requires an accurate dynamic model. While many theoretical frameworks have been proposed, experimentally validated models for hydrofoil vessels remain limited. This study aims to identify the pitch and height dynamics of the Flying Fish 1 hydrofoil demonstrator. For this, the closed loop system identification (CLSI) method in combination with a multisine excitation signal is used. The identified dynamics will be compared to a parametric dynamical model derived from literature. And, using the resulting frequency domain data, an attempt is made to improve the control performance of the pitch and height control loops.
The CLSI method, in combination with the multisine disturbance signal proved to be an effective method. Accurate frequency domain results were obtained within the frequency range of 1 to 10 Hz. Within this range, the parametric dynamic model aligned well with experimental data. The experiment was repeated three times at 4.0, 4.5 and 5.0 m/s and resulted in similar responses, aside from a negative trend in magnitude at increasing velocities. The use of frequency domain tuning led to an increase in phase margin for the pitch controller and a bandwidth increase of 667\% for the height controller. The results show that CLSI is a powerful tool in estimating the dynamics of a hydrofoil vessel, laying the foundation for advanced control strategies and improved system performance. ...
The CLSI method, in combination with the multisine disturbance signal proved to be an effective method. Accurate frequency domain results were obtained within the frequency range of 1 to 10 Hz. Within this range, the parametric dynamic model aligned well with experimental data. The experiment was repeated three times at 4.0, 4.5 and 5.0 m/s and resulted in similar responses, aside from a negative trend in magnitude at increasing velocities. The use of frequency domain tuning led to an increase in phase margin for the pitch controller and a bandwidth increase of 667\% for the height controller. The results show that CLSI is a powerful tool in estimating the dynamics of a hydrofoil vessel, laying the foundation for advanced control strategies and improved system performance. ...
Hydrofoil vessels show unstable behaviour when operating in foilborne condition. Therefore, an active control system (ACS) is used. Designing an ACS requires an accurate dynamic model. While many theoretical frameworks have been proposed, experimentally validated models for hydrofoil vessels remain limited. This study aims to identify the pitch and height dynamics of the Flying Fish 1 hydrofoil demonstrator. For this, the closed loop system identification (CLSI) method in combination with a multisine excitation signal is used. The identified dynamics will be compared to a parametric dynamical model derived from literature. And, using the resulting frequency domain data, an attempt is made to improve the control performance of the pitch and height control loops.
The CLSI method, in combination with the multisine disturbance signal proved to be an effective method. Accurate frequency domain results were obtained within the frequency range of 1 to 10 Hz. Within this range, the parametric dynamic model aligned well with experimental data. The experiment was repeated three times at 4.0, 4.5 and 5.0 m/s and resulted in similar responses, aside from a negative trend in magnitude at increasing velocities. The use of frequency domain tuning led to an increase in phase margin for the pitch controller and a bandwidth increase of 667\% for the height controller. The results show that CLSI is a powerful tool in estimating the dynamics of a hydrofoil vessel, laying the foundation for advanced control strategies and improved system performance.
The CLSI method, in combination with the multisine disturbance signal proved to be an effective method. Accurate frequency domain results were obtained within the frequency range of 1 to 10 Hz. Within this range, the parametric dynamic model aligned well with experimental data. The experiment was repeated three times at 4.0, 4.5 and 5.0 m/s and resulted in similar responses, aside from a negative trend in magnitude at increasing velocities. The use of frequency domain tuning led to an increase in phase margin for the pitch controller and a bandwidth increase of 667\% for the height controller. The results show that CLSI is a powerful tool in estimating the dynamics of a hydrofoil vessel, laying the foundation for advanced control strategies and improved system performance.
In this project, a data-driven modelling algorithm for learning new dynamical models from experimental magnetoencephalography (MEG) data is introduced. This algorithm provides a contrast to existing hypothesis-driven modelling techniques in neuronal dynamics, and is useful for generating new insights from data when hypotheses for the neural mechanisms underlying a process are not readily available. The algorithm utilises universal differential equations (UDEs), combining white-box modelling with machine learning techniques. The algorithm is applied to a single-subject human MEG dataset to produce an oscillator network model. The model captures the frequency-domain behaviour of and interaction between several brain regions of interest during completion of a working memory (WM) task. The machine learning techniques are used to identify the role of attention mechanisms in these interaction dynamics, providing neuroscientists with data-driven insights into the brain dynamics underlying the attention-WM interplay.
...
In this project, a data-driven modelling algorithm for learning new dynamical models from experimental magnetoencephalography (MEG) data is introduced. This algorithm provides a contrast to existing hypothesis-driven modelling techniques in neuronal dynamics, and is useful for generating new insights from data when hypotheses for the neural mechanisms underlying a process are not readily available. The algorithm utilises universal differential equations (UDEs), combining white-box modelling with machine learning techniques. The algorithm is applied to a single-subject human MEG dataset to produce an oscillator network model. The model captures the frequency-domain behaviour of and interaction between several brain regions of interest during completion of a working memory (WM) task. The machine learning techniques are used to identify the role of attention mechanisms in these interaction dynamics, providing neuroscientists with data-driven insights into the brain dynamics underlying the attention-WM interplay.
Stroke survivors often exhibit motor impairments, which hinder activities of daily living. While grounded robotic perturbation devices can accurately quantify joint dynamics via system identification, their size and fixed positioning limit functional assessments under realistic conditions. To address this gap, we present the design and pilot evaluation of a novel, wearable perturbation device capable of delivering ungrounded force perturbations to the user’s forearm.
The device uses a linear solenoid actuator housed in a wrist brace to generate short, pulse-type forces, thereby inducing small angular deflections (approximately 1–3°) to the arm. An inertial measurement unit (IMU) placed on the brace tracks the resulting movement, while an accelerometer on the solenoid coil measures the perturbation force. Nine healthy participants performed three tasks—relax, resist, and move. Random pulse signals were used to prevent anticipation of the perturbations. The device successfully deflected the arm in all tasks. The largest deflections was recorded during the relax task and smaller, though still measurable, deflections in the resist and move tasks.
Estimated stiffness values in each task indicated that the device could distinguish different levels of joint rigidity, although comparisons with established literature showed some over- or underestimation. Factors such as non-rigid brace attachment and off-center actuator placement contributed to these discrepancies. Despite these limitations, the prototype demonstrates the feasibility of wearable, ungrounded force perturbations for assessing elbow dynamics. Future work will focus on improving the device’s rigidity, exploring multi-degree-of-freedom perturbations, and refining stiffness estimation algorithms to better capture realistic joint behaviors. ...
The device uses a linear solenoid actuator housed in a wrist brace to generate short, pulse-type forces, thereby inducing small angular deflections (approximately 1–3°) to the arm. An inertial measurement unit (IMU) placed on the brace tracks the resulting movement, while an accelerometer on the solenoid coil measures the perturbation force. Nine healthy participants performed three tasks—relax, resist, and move. Random pulse signals were used to prevent anticipation of the perturbations. The device successfully deflected the arm in all tasks. The largest deflections was recorded during the relax task and smaller, though still measurable, deflections in the resist and move tasks.
Estimated stiffness values in each task indicated that the device could distinguish different levels of joint rigidity, although comparisons with established literature showed some over- or underestimation. Factors such as non-rigid brace attachment and off-center actuator placement contributed to these discrepancies. Despite these limitations, the prototype demonstrates the feasibility of wearable, ungrounded force perturbations for assessing elbow dynamics. Future work will focus on improving the device’s rigidity, exploring multi-degree-of-freedom perturbations, and refining stiffness estimation algorithms to better capture realistic joint behaviors. ...
Stroke survivors often exhibit motor impairments, which hinder activities of daily living. While grounded robotic perturbation devices can accurately quantify joint dynamics via system identification, their size and fixed positioning limit functional assessments under realistic conditions. To address this gap, we present the design and pilot evaluation of a novel, wearable perturbation device capable of delivering ungrounded force perturbations to the user’s forearm.
The device uses a linear solenoid actuator housed in a wrist brace to generate short, pulse-type forces, thereby inducing small angular deflections (approximately 1–3°) to the arm. An inertial measurement unit (IMU) placed on the brace tracks the resulting movement, while an accelerometer on the solenoid coil measures the perturbation force. Nine healthy participants performed three tasks—relax, resist, and move. Random pulse signals were used to prevent anticipation of the perturbations. The device successfully deflected the arm in all tasks. The largest deflections was recorded during the relax task and smaller, though still measurable, deflections in the resist and move tasks.
Estimated stiffness values in each task indicated that the device could distinguish different levels of joint rigidity, although comparisons with established literature showed some over- or underestimation. Factors such as non-rigid brace attachment and off-center actuator placement contributed to these discrepancies. Despite these limitations, the prototype demonstrates the feasibility of wearable, ungrounded force perturbations for assessing elbow dynamics. Future work will focus on improving the device’s rigidity, exploring multi-degree-of-freedom perturbations, and refining stiffness estimation algorithms to better capture realistic joint behaviors.
The device uses a linear solenoid actuator housed in a wrist brace to generate short, pulse-type forces, thereby inducing small angular deflections (approximately 1–3°) to the arm. An inertial measurement unit (IMU) placed on the brace tracks the resulting movement, while an accelerometer on the solenoid coil measures the perturbation force. Nine healthy participants performed three tasks—relax, resist, and move. Random pulse signals were used to prevent anticipation of the perturbations. The device successfully deflected the arm in all tasks. The largest deflections was recorded during the relax task and smaller, though still measurable, deflections in the resist and move tasks.
Estimated stiffness values in each task indicated that the device could distinguish different levels of joint rigidity, although comparisons with established literature showed some over- or underestimation. Factors such as non-rigid brace attachment and off-center actuator placement contributed to these discrepancies. Despite these limitations, the prototype demonstrates the feasibility of wearable, ungrounded force perturbations for assessing elbow dynamics. Future work will focus on improving the device’s rigidity, exploring multi-degree-of-freedom perturbations, and refining stiffness estimation algorithms to better capture realistic joint behaviors.
Predicting extubation readiness is a major challenge in pediatric intensive care, as inaccurate assessments often lead to reintubation or prolonged mechanical ventilation, resulting in worse clinical outcomes. Surface electromyography (sEMG) offers a noninvasive approach to quantify diaphragm activation and potentially assess extubation readiness. This exploratory study evaluated whether diaphragm sEMG-derived parameters can predict extubation outcome in mechanically ventilated infants.
Methods: sEMG signals were recorded using the Dipha-16 (by Demcon Macawi) device in 35 infants (<1 year) before, during and after a standardized Extubation Readiness Test (ERT). Parameters including tonic and peak activity, inspiratory AUC, entropy, respiratory rate, minute volume, inspiratory duty cycle and neuroventilatory efficiency were extracted using a custom-made Python processing pipeline. Statistical comparisons between extubation success and failure groups (23 vs 12 infants) included Mann–Whitney U tests on absolute and normalized values, and on intra-ERT trends.
Results: No significant results were found when comparing extubation outcome groups between all time point phases of all the absolute and normalized parameters. The tonic sEMG activity displayed the highest discriminatory power on the ROC curve (AUC = 0.65). Across the ERT, the inspiratory AUC and minute volume showed significant increasing slopes for the failure group.
Conclusions: The sEMG parameters derived in this study can not predict extubation outcome, but trends indicate that diaphragm activity patterns differ between outcome groups. Future large cohort studies are required that include occlusion manoeuvres within the protocol, longer pre- and post-ERT measurements, high-frequency flow references, and advanced real- time artifact removal methods or objective artifact-free selection criteria, all tailored to infants. ...
Methods: sEMG signals were recorded using the Dipha-16 (by Demcon Macawi) device in 35 infants (<1 year) before, during and after a standardized Extubation Readiness Test (ERT). Parameters including tonic and peak activity, inspiratory AUC, entropy, respiratory rate, minute volume, inspiratory duty cycle and neuroventilatory efficiency were extracted using a custom-made Python processing pipeline. Statistical comparisons between extubation success and failure groups (23 vs 12 infants) included Mann–Whitney U tests on absolute and normalized values, and on intra-ERT trends.
Results: No significant results were found when comparing extubation outcome groups between all time point phases of all the absolute and normalized parameters. The tonic sEMG activity displayed the highest discriminatory power on the ROC curve (AUC = 0.65). Across the ERT, the inspiratory AUC and minute volume showed significant increasing slopes for the failure group.
Conclusions: The sEMG parameters derived in this study can not predict extubation outcome, but trends indicate that diaphragm activity patterns differ between outcome groups. Future large cohort studies are required that include occlusion manoeuvres within the protocol, longer pre- and post-ERT measurements, high-frequency flow references, and advanced real- time artifact removal methods or objective artifact-free selection criteria, all tailored to infants. ...
Predicting extubation readiness is a major challenge in pediatric intensive care, as inaccurate assessments often lead to reintubation or prolonged mechanical ventilation, resulting in worse clinical outcomes. Surface electromyography (sEMG) offers a noninvasive approach to quantify diaphragm activation and potentially assess extubation readiness. This exploratory study evaluated whether diaphragm sEMG-derived parameters can predict extubation outcome in mechanically ventilated infants.
Methods: sEMG signals were recorded using the Dipha-16 (by Demcon Macawi) device in 35 infants (<1 year) before, during and after a standardized Extubation Readiness Test (ERT). Parameters including tonic and peak activity, inspiratory AUC, entropy, respiratory rate, minute volume, inspiratory duty cycle and neuroventilatory efficiency were extracted using a custom-made Python processing pipeline. Statistical comparisons between extubation success and failure groups (23 vs 12 infants) included Mann–Whitney U tests on absolute and normalized values, and on intra-ERT trends.
Results: No significant results were found when comparing extubation outcome groups between all time point phases of all the absolute and normalized parameters. The tonic sEMG activity displayed the highest discriminatory power on the ROC curve (AUC = 0.65). Across the ERT, the inspiratory AUC and minute volume showed significant increasing slopes for the failure group.
Conclusions: The sEMG parameters derived in this study can not predict extubation outcome, but trends indicate that diaphragm activity patterns differ between outcome groups. Future large cohort studies are required that include occlusion manoeuvres within the protocol, longer pre- and post-ERT measurements, high-frequency flow references, and advanced real- time artifact removal methods or objective artifact-free selection criteria, all tailored to infants.
Methods: sEMG signals were recorded using the Dipha-16 (by Demcon Macawi) device in 35 infants (<1 year) before, during and after a standardized Extubation Readiness Test (ERT). Parameters including tonic and peak activity, inspiratory AUC, entropy, respiratory rate, minute volume, inspiratory duty cycle and neuroventilatory efficiency were extracted using a custom-made Python processing pipeline. Statistical comparisons between extubation success and failure groups (23 vs 12 infants) included Mann–Whitney U tests on absolute and normalized values, and on intra-ERT trends.
Results: No significant results were found when comparing extubation outcome groups between all time point phases of all the absolute and normalized parameters. The tonic sEMG activity displayed the highest discriminatory power on the ROC curve (AUC = 0.65). Across the ERT, the inspiratory AUC and minute volume showed significant increasing slopes for the failure group.
Conclusions: The sEMG parameters derived in this study can not predict extubation outcome, but trends indicate that diaphragm activity patterns differ between outcome groups. Future large cohort studies are required that include occlusion manoeuvres within the protocol, longer pre- and post-ERT measurements, high-frequency flow references, and advanced real- time artifact removal methods or objective artifact-free selection criteria, all tailored to infants.
Musculoskeletal modelling forms an important asset in research on understanding human motions and neuromuscular performance. The reliability of musculoskeletal model outcomes depend on the accuracy of the parameters, particularly Optimal Fiber Length (OFL). However, the OFL may vary considerably between populations. To date it is unknown how such variations might affect musculoskeletal modelling. Therefore, in vivo determination of OFL is essential for subject-specific models, and understanding population-specific differences for improvement of model diversity. This study developed an in vivo methodology for measuring the OFL that is easily accessible for large scale implementations. It was applied it to the m. rectus femoris for validation.
OFL was derived from the force-length relationship by measuring muscle force, calculated from knee moments, muscle-tendon moment arm, and fascicle length using ultrasound. Muscle activation was standardized via electrical stimulation. The protocol was separately evaluated for validity, reliability, and usability.
Results indicated that knee moment and muscle-tendon moment arm measurements deviated from literature values due to experimental setup limitation, and active fascicle lengths could not be reliably estimated due to the complex muscle architecture. Consequently, the current approach did not yield valid OFL estimates. This study provides insight into the challenges of developing reliable in vivo measurement techniques.
Future studies should employ improved experimental approaches such as increasing electrical muscle stimulation, use of dynamometry and more advanced ultrasound techniques, and applications to other muscles and joints, ultimately, providing a foundation for in vivo estimation of OFL, facilitating investigation of population-specific differences and improving diversity in musculoskeletal modelling. ...
OFL was derived from the force-length relationship by measuring muscle force, calculated from knee moments, muscle-tendon moment arm, and fascicle length using ultrasound. Muscle activation was standardized via electrical stimulation. The protocol was separately evaluated for validity, reliability, and usability.
Results indicated that knee moment and muscle-tendon moment arm measurements deviated from literature values due to experimental setup limitation, and active fascicle lengths could not be reliably estimated due to the complex muscle architecture. Consequently, the current approach did not yield valid OFL estimates. This study provides insight into the challenges of developing reliable in vivo measurement techniques.
Future studies should employ improved experimental approaches such as increasing electrical muscle stimulation, use of dynamometry and more advanced ultrasound techniques, and applications to other muscles and joints, ultimately, providing a foundation for in vivo estimation of OFL, facilitating investigation of population-specific differences and improving diversity in musculoskeletal modelling. ...
Musculoskeletal modelling forms an important asset in research on understanding human motions and neuromuscular performance. The reliability of musculoskeletal model outcomes depend on the accuracy of the parameters, particularly Optimal Fiber Length (OFL). However, the OFL may vary considerably between populations. To date it is unknown how such variations might affect musculoskeletal modelling. Therefore, in vivo determination of OFL is essential for subject-specific models, and understanding population-specific differences for improvement of model diversity. This study developed an in vivo methodology for measuring the OFL that is easily accessible for large scale implementations. It was applied it to the m. rectus femoris for validation.
OFL was derived from the force-length relationship by measuring muscle force, calculated from knee moments, muscle-tendon moment arm, and fascicle length using ultrasound. Muscle activation was standardized via electrical stimulation. The protocol was separately evaluated for validity, reliability, and usability.
Results indicated that knee moment and muscle-tendon moment arm measurements deviated from literature values due to experimental setup limitation, and active fascicle lengths could not be reliably estimated due to the complex muscle architecture. Consequently, the current approach did not yield valid OFL estimates. This study provides insight into the challenges of developing reliable in vivo measurement techniques.
Future studies should employ improved experimental approaches such as increasing electrical muscle stimulation, use of dynamometry and more advanced ultrasound techniques, and applications to other muscles and joints, ultimately, providing a foundation for in vivo estimation of OFL, facilitating investigation of population-specific differences and improving diversity in musculoskeletal modelling.
OFL was derived from the force-length relationship by measuring muscle force, calculated from knee moments, muscle-tendon moment arm, and fascicle length using ultrasound. Muscle activation was standardized via electrical stimulation. The protocol was separately evaluated for validity, reliability, and usability.
Results indicated that knee moment and muscle-tendon moment arm measurements deviated from literature values due to experimental setup limitation, and active fascicle lengths could not be reliably estimated due to the complex muscle architecture. Consequently, the current approach did not yield valid OFL estimates. This study provides insight into the challenges of developing reliable in vivo measurement techniques.
Future studies should employ improved experimental approaches such as increasing electrical muscle stimulation, use of dynamometry and more advanced ultrasound techniques, and applications to other muscles and joints, ultimately, providing a foundation for in vivo estimation of OFL, facilitating investigation of population-specific differences and improving diversity in musculoskeletal modelling.
Running is one of the most practiced sports worldwide, offering numerous health benefits, but also carrying a risk of injury, mainly at the knee and ankle joints. The origin of running injuries is not fully understood. With predictive neuromusculoskeletal simulations, more insight could be gained into the biomechanical mechanisms that may lead to injuries.
However, in predictive simulations of gait, hyperextension of the knee during stance phase is often encountered. This limits their applicability in research into running-related injuries. It is unclear what causes these unrealistic kinematics, with various studies coming to conflicting conclusions.
This study aims to identify the cause of knee hyperextension in predictive models of running and subsequently, to determine the essential modeling elements for accurately simulating stance knee flexion.
A structured analysis was conducted to investigate the potential impact of the model components within the predictive simulation framework. This framework was divided into four main categories: the objective function, the musculoskeletal (MSK) model, the foot contact model, and the controller. The analysis resulted in numerous hypotheses regarding the element that might be responsible for the simulation of realistic knee kinematics. SCONE, an open-source package for neuromusculoskeletal predictive simulation, was used to test the effect of each hypothesis on the simulated running kinematics. The simulation outcomes were compared to experimental data to assess possible improvements.
The results demonstrate that, in contrast to previous literature, adaptations to the objective function, the MSK model, and the foot contact model have negligible effects on predicted running kinematics. This leads to the conclusion that the controller is essential to focus on when improving knee kinematics. Due to time constraints, multiphase control could not be implemented. Therefore, the exact reflex pathways and phase transitions should be further investigated for the predictive simulation of running before implementation is possible. ...
However, in predictive simulations of gait, hyperextension of the knee during stance phase is often encountered. This limits their applicability in research into running-related injuries. It is unclear what causes these unrealistic kinematics, with various studies coming to conflicting conclusions.
This study aims to identify the cause of knee hyperextension in predictive models of running and subsequently, to determine the essential modeling elements for accurately simulating stance knee flexion.
A structured analysis was conducted to investigate the potential impact of the model components within the predictive simulation framework. This framework was divided into four main categories: the objective function, the musculoskeletal (MSK) model, the foot contact model, and the controller. The analysis resulted in numerous hypotheses regarding the element that might be responsible for the simulation of realistic knee kinematics. SCONE, an open-source package for neuromusculoskeletal predictive simulation, was used to test the effect of each hypothesis on the simulated running kinematics. The simulation outcomes were compared to experimental data to assess possible improvements.
The results demonstrate that, in contrast to previous literature, adaptations to the objective function, the MSK model, and the foot contact model have negligible effects on predicted running kinematics. This leads to the conclusion that the controller is essential to focus on when improving knee kinematics. Due to time constraints, multiphase control could not be implemented. Therefore, the exact reflex pathways and phase transitions should be further investigated for the predictive simulation of running before implementation is possible. ...
Running is one of the most practiced sports worldwide, offering numerous health benefits, but also carrying a risk of injury, mainly at the knee and ankle joints. The origin of running injuries is not fully understood. With predictive neuromusculoskeletal simulations, more insight could be gained into the biomechanical mechanisms that may lead to injuries.
However, in predictive simulations of gait, hyperextension of the knee during stance phase is often encountered. This limits their applicability in research into running-related injuries. It is unclear what causes these unrealistic kinematics, with various studies coming to conflicting conclusions.
This study aims to identify the cause of knee hyperextension in predictive models of running and subsequently, to determine the essential modeling elements for accurately simulating stance knee flexion.
A structured analysis was conducted to investigate the potential impact of the model components within the predictive simulation framework. This framework was divided into four main categories: the objective function, the musculoskeletal (MSK) model, the foot contact model, and the controller. The analysis resulted in numerous hypotheses regarding the element that might be responsible for the simulation of realistic knee kinematics. SCONE, an open-source package for neuromusculoskeletal predictive simulation, was used to test the effect of each hypothesis on the simulated running kinematics. The simulation outcomes were compared to experimental data to assess possible improvements.
The results demonstrate that, in contrast to previous literature, adaptations to the objective function, the MSK model, and the foot contact model have negligible effects on predicted running kinematics. This leads to the conclusion that the controller is essential to focus on when improving knee kinematics. Due to time constraints, multiphase control could not be implemented. Therefore, the exact reflex pathways and phase transitions should be further investigated for the predictive simulation of running before implementation is possible.
However, in predictive simulations of gait, hyperextension of the knee during stance phase is often encountered. This limits their applicability in research into running-related injuries. It is unclear what causes these unrealistic kinematics, with various studies coming to conflicting conclusions.
This study aims to identify the cause of knee hyperextension in predictive models of running and subsequently, to determine the essential modeling elements for accurately simulating stance knee flexion.
A structured analysis was conducted to investigate the potential impact of the model components within the predictive simulation framework. This framework was divided into four main categories: the objective function, the musculoskeletal (MSK) model, the foot contact model, and the controller. The analysis resulted in numerous hypotheses regarding the element that might be responsible for the simulation of realistic knee kinematics. SCONE, an open-source package for neuromusculoskeletal predictive simulation, was used to test the effect of each hypothesis on the simulated running kinematics. The simulation outcomes were compared to experimental data to assess possible improvements.
The results demonstrate that, in contrast to previous literature, adaptations to the objective function, the MSK model, and the foot contact model have negligible effects on predicted running kinematics. This leads to the conclusion that the controller is essential to focus on when improving knee kinematics. Due to time constraints, multiphase control could not be implemented. Therefore, the exact reflex pathways and phase transitions should be further investigated for the predictive simulation of running before implementation is possible.
Master thesis
(2023)
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J.A.M. Luijten, C.C. de Vos, A.C. Schouten, S.P.G. Frankema, M.L. van de Ruit
Introduction
Chronic pain is an increasing problem in terms of prevalence and disease-related costs. Due to its complexity, it is difficult to treat. Spinal cord stimulation (SCS) is a neurostimulation therapy with a relatively good success rate for patients with severe, intractable chronic pain. The mechanisms of action (MOAs) of SCS are considered to rely on spinal and supraspinal mechanisms. It has been suggested that newer SCS paradigms, such as burst SCS, may act through different MOAs than the traditional tonic SCS paradigm. Tonic and burst SCS are both postulated to act on the lateral pain pathway, which is associated with the location and character of a stimulus, whereas burst SCS is postulated to additionally act on the medial pain pathway, which is associated with the emotional/attentional processing of a stimulus. Somatosensory evoked responses (SERs) can be used to evaluate the processing of somatosensory stimuli and may aid in the unraveling of the MOAs of SCS.
Aim
The aim of this thesis is to assess how burst and tonic SCS affect the supraspinal SERs elicited by non-painful transcutaneous electrical stimulation of the tibial nerve as well as of the median nerve. The two distinct SERs are evaluated using magnetoencephalography (MEG).
Methods
26 chronic pain patients treated with SCS underwent MEG sessions after receiving tonic and burst SCS for one week. Four of these patients additionally underwent a MEG session before SCS treatment. During each session, approximately 200 non-painful electrical stimuli were applied to the median nerve as well as to the tibial nerve to elicit SERs. The SERs were compared in various cortical and subcortical regions of interest (ROIs). The following comparisons were made: 1) SERs in chronic pain patients before SCS implantation versus SERs in the same individuals during SCS, 2) SERs elicited by tibial nerve stimulation versus SERs elicited by median nerve stimulation, 3) SERs during tonic SCS versus SERs during burst SCS, and 4) the SERs in four case studies of two good and two poor responders to the tonic and/or burst SCS paradigms.
Results
22 patients were included for analysis. The number of patients varied among comparisons to facilitate within-patient comparisons. The results suggested an inhibitory effect of SCS on the SER elicited by tibial nerve stimulation, whereas the amplitude of the SER elicited by median nerve stimulation tended to increase during SCS. For both the SERs elicited by tibial nerve and by median nerve stimulation, the SER amplitudes were predominantly higher during burst SCS compared to tonic SCS. Differences in SER amplitude that were observed in the case studies did not correlate with pain relief.
Conclusion
The results suggested a spinal MOA of SCS on the SER, however, supraspinal MOAs likely play a role as well. The results did not suggest that burst SCS additionally acts on the emotional/attentional processing compared to tonic SCS. No evidence was found to support a correlation between the effect of SCS on the SER and the effect of SCS on the pain, underscoring the complexity of the relationship between somatosensory processing and pain perception in the context of SCS.
...
Chronic pain is an increasing problem in terms of prevalence and disease-related costs. Due to its complexity, it is difficult to treat. Spinal cord stimulation (SCS) is a neurostimulation therapy with a relatively good success rate for patients with severe, intractable chronic pain. The mechanisms of action (MOAs) of SCS are considered to rely on spinal and supraspinal mechanisms. It has been suggested that newer SCS paradigms, such as burst SCS, may act through different MOAs than the traditional tonic SCS paradigm. Tonic and burst SCS are both postulated to act on the lateral pain pathway, which is associated with the location and character of a stimulus, whereas burst SCS is postulated to additionally act on the medial pain pathway, which is associated with the emotional/attentional processing of a stimulus. Somatosensory evoked responses (SERs) can be used to evaluate the processing of somatosensory stimuli and may aid in the unraveling of the MOAs of SCS.
Aim
The aim of this thesis is to assess how burst and tonic SCS affect the supraspinal SERs elicited by non-painful transcutaneous electrical stimulation of the tibial nerve as well as of the median nerve. The two distinct SERs are evaluated using magnetoencephalography (MEG).
Methods
26 chronic pain patients treated with SCS underwent MEG sessions after receiving tonic and burst SCS for one week. Four of these patients additionally underwent a MEG session before SCS treatment. During each session, approximately 200 non-painful electrical stimuli were applied to the median nerve as well as to the tibial nerve to elicit SERs. The SERs were compared in various cortical and subcortical regions of interest (ROIs). The following comparisons were made: 1) SERs in chronic pain patients before SCS implantation versus SERs in the same individuals during SCS, 2) SERs elicited by tibial nerve stimulation versus SERs elicited by median nerve stimulation, 3) SERs during tonic SCS versus SERs during burst SCS, and 4) the SERs in four case studies of two good and two poor responders to the tonic and/or burst SCS paradigms.
Results
22 patients were included for analysis. The number of patients varied among comparisons to facilitate within-patient comparisons. The results suggested an inhibitory effect of SCS on the SER elicited by tibial nerve stimulation, whereas the amplitude of the SER elicited by median nerve stimulation tended to increase during SCS. For both the SERs elicited by tibial nerve and by median nerve stimulation, the SER amplitudes were predominantly higher during burst SCS compared to tonic SCS. Differences in SER amplitude that were observed in the case studies did not correlate with pain relief.
Conclusion
The results suggested a spinal MOA of SCS on the SER, however, supraspinal MOAs likely play a role as well. The results did not suggest that burst SCS additionally acts on the emotional/attentional processing compared to tonic SCS. No evidence was found to support a correlation between the effect of SCS on the SER and the effect of SCS on the pain, underscoring the complexity of the relationship between somatosensory processing and pain perception in the context of SCS.
...
Introduction
Chronic pain is an increasing problem in terms of prevalence and disease-related costs. Due to its complexity, it is difficult to treat. Spinal cord stimulation (SCS) is a neurostimulation therapy with a relatively good success rate for patients with severe, intractable chronic pain. The mechanisms of action (MOAs) of SCS are considered to rely on spinal and supraspinal mechanisms. It has been suggested that newer SCS paradigms, such as burst SCS, may act through different MOAs than the traditional tonic SCS paradigm. Tonic and burst SCS are both postulated to act on the lateral pain pathway, which is associated with the location and character of a stimulus, whereas burst SCS is postulated to additionally act on the medial pain pathway, which is associated with the emotional/attentional processing of a stimulus. Somatosensory evoked responses (SERs) can be used to evaluate the processing of somatosensory stimuli and may aid in the unraveling of the MOAs of SCS.
Aim
The aim of this thesis is to assess how burst and tonic SCS affect the supraspinal SERs elicited by non-painful transcutaneous electrical stimulation of the tibial nerve as well as of the median nerve. The two distinct SERs are evaluated using magnetoencephalography (MEG).
Methods
26 chronic pain patients treated with SCS underwent MEG sessions after receiving tonic and burst SCS for one week. Four of these patients additionally underwent a MEG session before SCS treatment. During each session, approximately 200 non-painful electrical stimuli were applied to the median nerve as well as to the tibial nerve to elicit SERs. The SERs were compared in various cortical and subcortical regions of interest (ROIs). The following comparisons were made: 1) SERs in chronic pain patients before SCS implantation versus SERs in the same individuals during SCS, 2) SERs elicited by tibial nerve stimulation versus SERs elicited by median nerve stimulation, 3) SERs during tonic SCS versus SERs during burst SCS, and 4) the SERs in four case studies of two good and two poor responders to the tonic and/or burst SCS paradigms.
Results
22 patients were included for analysis. The number of patients varied among comparisons to facilitate within-patient comparisons. The results suggested an inhibitory effect of SCS on the SER elicited by tibial nerve stimulation, whereas the amplitude of the SER elicited by median nerve stimulation tended to increase during SCS. For both the SERs elicited by tibial nerve and by median nerve stimulation, the SER amplitudes were predominantly higher during burst SCS compared to tonic SCS. Differences in SER amplitude that were observed in the case studies did not correlate with pain relief.
Conclusion
The results suggested a spinal MOA of SCS on the SER, however, supraspinal MOAs likely play a role as well. The results did not suggest that burst SCS additionally acts on the emotional/attentional processing compared to tonic SCS. No evidence was found to support a correlation between the effect of SCS on the SER and the effect of SCS on the pain, underscoring the complexity of the relationship between somatosensory processing and pain perception in the context of SCS.
Chronic pain is an increasing problem in terms of prevalence and disease-related costs. Due to its complexity, it is difficult to treat. Spinal cord stimulation (SCS) is a neurostimulation therapy with a relatively good success rate for patients with severe, intractable chronic pain. The mechanisms of action (MOAs) of SCS are considered to rely on spinal and supraspinal mechanisms. It has been suggested that newer SCS paradigms, such as burst SCS, may act through different MOAs than the traditional tonic SCS paradigm. Tonic and burst SCS are both postulated to act on the lateral pain pathway, which is associated with the location and character of a stimulus, whereas burst SCS is postulated to additionally act on the medial pain pathway, which is associated with the emotional/attentional processing of a stimulus. Somatosensory evoked responses (SERs) can be used to evaluate the processing of somatosensory stimuli and may aid in the unraveling of the MOAs of SCS.
Aim
The aim of this thesis is to assess how burst and tonic SCS affect the supraspinal SERs elicited by non-painful transcutaneous electrical stimulation of the tibial nerve as well as of the median nerve. The two distinct SERs are evaluated using magnetoencephalography (MEG).
Methods
26 chronic pain patients treated with SCS underwent MEG sessions after receiving tonic and burst SCS for one week. Four of these patients additionally underwent a MEG session before SCS treatment. During each session, approximately 200 non-painful electrical stimuli were applied to the median nerve as well as to the tibial nerve to elicit SERs. The SERs were compared in various cortical and subcortical regions of interest (ROIs). The following comparisons were made: 1) SERs in chronic pain patients before SCS implantation versus SERs in the same individuals during SCS, 2) SERs elicited by tibial nerve stimulation versus SERs elicited by median nerve stimulation, 3) SERs during tonic SCS versus SERs during burst SCS, and 4) the SERs in four case studies of two good and two poor responders to the tonic and/or burst SCS paradigms.
Results
22 patients were included for analysis. The number of patients varied among comparisons to facilitate within-patient comparisons. The results suggested an inhibitory effect of SCS on the SER elicited by tibial nerve stimulation, whereas the amplitude of the SER elicited by median nerve stimulation tended to increase during SCS. For both the SERs elicited by tibial nerve and by median nerve stimulation, the SER amplitudes were predominantly higher during burst SCS compared to tonic SCS. Differences in SER amplitude that were observed in the case studies did not correlate with pain relief.
Conclusion
The results suggested a spinal MOA of SCS on the SER, however, supraspinal MOAs likely play a role as well. The results did not suggest that burst SCS additionally acts on the emotional/attentional processing compared to tonic SCS. No evidence was found to support a correlation between the effect of SCS on the SER and the effect of SCS on the pain, underscoring the complexity of the relationship between somatosensory processing and pain perception in the context of SCS.
Master thesis
(2020)
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Jaya Rupini Vijayaragavan, F.C.T. van der Helm, Luigi Gallo, M.L. van de Ruit, M. Jafarian
Temporomandibular disorders (TMD) affect about 5-12 percentage of individuals with consequences such as jaw noises, clicking, myofascial pain, discomfort, limited mandibular range of motion and stress. Treatments depend on the cause and extent of the damage and part of the joint or jaw affected. When exact aetiology of TMD is unclear, generic treatments (splint therapy) are offered. Different oral activities performed on a daily-basis result in different loading conditions on the joint, possibly triggering TMD. These need to be investigated to know the usage of the masticatory system and the potential damage, in order to perform specific treatments. Our work aims at developing an online algorithm that can classify oral tasks performed by individuals. It can be used during daytime or overnight’s sleep to see how often different activities are performed by subjects. A 4 stage wavelet decomposition was employed to the signals and then subjected to feature extraction to train a support vector machine algorithm with. The prediction accuracy was found to be 90 percent for a group of selected oral activities (static, jaw opening, chewing and maximal voluntary clenching). The algorithm had about 80 percent prediction accuracy when classifying both functional (chewing, jaw opening and static) and parafunctional activities (grinding, incisal biting, maximal voluntary clenching, protrusion and laterotrusion) together. However, 80 percent accuracy is regarded as a set back due to the lack of more data. On reviewing the recognised activities, further research on any overuse of muscles or loading on the jaw joint during each activity can be conducted to give specific treatment and therapy preventing any deteriorating actions. Thus,the developed classification algorithm works as a prototype for future studies on online recognition of oral activities.
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Temporomandibular disorders (TMD) affect about 5-12 percentage of individuals with consequences such as jaw noises, clicking, myofascial pain, discomfort, limited mandibular range of motion and stress. Treatments depend on the cause and extent of the damage and part of the joint or jaw affected. When exact aetiology of TMD is unclear, generic treatments (splint therapy) are offered. Different oral activities performed on a daily-basis result in different loading conditions on the joint, possibly triggering TMD. These need to be investigated to know the usage of the masticatory system and the potential damage, in order to perform specific treatments. Our work aims at developing an online algorithm that can classify oral tasks performed by individuals. It can be used during daytime or overnight’s sleep to see how often different activities are performed by subjects. A 4 stage wavelet decomposition was employed to the signals and then subjected to feature extraction to train a support vector machine algorithm with. The prediction accuracy was found to be 90 percent for a group of selected oral activities (static, jaw opening, chewing and maximal voluntary clenching). The algorithm had about 80 percent prediction accuracy when classifying both functional (chewing, jaw opening and static) and parafunctional activities (grinding, incisal biting, maximal voluntary clenching, protrusion and laterotrusion) together. However, 80 percent accuracy is regarded as a set back due to the lack of more data. On reviewing the recognised activities, further research on any overuse of muscles or loading on the jaw joint during each activity can be conducted to give specific treatment and therapy preventing any deteriorating actions. Thus,the developed classification algorithm works as a prototype for future studies on online recognition of oral activities.
Master thesis
(2018)
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Annemijn Smid, Alfred Schouten, Thijs Perenboom, Caroline Wehrmann, Mark van de Ruit
Migraine is associated with brain dysfunction, possibly due to disturbances in the interactions between distributed cortical regions. Detection of these disturbances in the topological organization of the brain’s functional network would contribute to further understanding of migraine pathophysiology. Altered cortical responses to external stimulation of different modalities are observed in migraine patients, also between attacks (in the interictal state). However, it is yet unclear if abnormalities are detectable in the functional network at rest, i.e. without external stimulation. Here, we assessed abnormalities in migraine functional networks on a global and a local level, based on resting state electroencephalography (EEG) data and graph analysis. Scalp-wide (128-channel)
eyes closed EEG was recorded in 18 episodic migraine patients with and without
aura and 15 healthy controls. We calculated functional connectivity based on
coherence and phase-lag index, and performed graph analysis to characterize
network topology. The minimum spanning tree, a subgraph with maximum
functional connectivity, was used for comparison. No significant differences were
found in network topology, nor in functional connectivity strength between groups. These results demonstrate that this type of graph analyses are not sensitive to any possible abnormalities in the interictal migraine functional network in resting state. Brain dysfunction in migraine might occur only on a local level, making EEG-based graph analysis a less suitable technique to uncover such abnormalities. ...
eyes closed EEG was recorded in 18 episodic migraine patients with and without
aura and 15 healthy controls. We calculated functional connectivity based on
coherence and phase-lag index, and performed graph analysis to characterize
network topology. The minimum spanning tree, a subgraph with maximum
functional connectivity, was used for comparison. No significant differences were
found in network topology, nor in functional connectivity strength between groups. These results demonstrate that this type of graph analyses are not sensitive to any possible abnormalities in the interictal migraine functional network in resting state. Brain dysfunction in migraine might occur only on a local level, making EEG-based graph analysis a less suitable technique to uncover such abnormalities. ...
Migraine is associated with brain dysfunction, possibly due to disturbances in the interactions between distributed cortical regions. Detection of these disturbances in the topological organization of the brain’s functional network would contribute to further understanding of migraine pathophysiology. Altered cortical responses to external stimulation of different modalities are observed in migraine patients, also between attacks (in the interictal state). However, it is yet unclear if abnormalities are detectable in the functional network at rest, i.e. without external stimulation. Here, we assessed abnormalities in migraine functional networks on a global and a local level, based on resting state electroencephalography (EEG) data and graph analysis. Scalp-wide (128-channel)
eyes closed EEG was recorded in 18 episodic migraine patients with and without
aura and 15 healthy controls. We calculated functional connectivity based on
coherence and phase-lag index, and performed graph analysis to characterize
network topology. The minimum spanning tree, a subgraph with maximum
functional connectivity, was used for comparison. No significant differences were
found in network topology, nor in functional connectivity strength between groups. These results demonstrate that this type of graph analyses are not sensitive to any possible abnormalities in the interictal migraine functional network in resting state. Brain dysfunction in migraine might occur only on a local level, making EEG-based graph analysis a less suitable technique to uncover such abnormalities.
eyes closed EEG was recorded in 18 episodic migraine patients with and without
aura and 15 healthy controls. We calculated functional connectivity based on
coherence and phase-lag index, and performed graph analysis to characterize
network topology. The minimum spanning tree, a subgraph with maximum
functional connectivity, was used for comparison. No significant differences were
found in network topology, nor in functional connectivity strength between groups. These results demonstrate that this type of graph analyses are not sensitive to any possible abnormalities in the interictal migraine functional network in resting state. Brain dysfunction in migraine might occur only on a local level, making EEG-based graph analysis a less suitable technique to uncover such abnormalities.
By monitoring head movement and orientation in space, the vestibular system can evoke appropriate muscle responses in order to maintain standing balance. The present study investigates whether vestibular-evoked muscle responses are dependent on sensory cues of gravity by examining these responses across varying load and gravity conditions. Standing subjects were exposed to a stochastic electrical vestibular stimulus (EVS, ±5 mA, 0-25 Hz) that induced a vestibular error signal, while vertical loading forces or vestibular signals of gravity were independently modified. A backboard structure limited subjects’ whole-body rotation to the sagittal plane which corresponded with the EVS-evoked sway responses in anteroposterior direction, as the subject’s head was rotated in yaw. Vestibular-evoked muscle responses were greatest when sensory cues of gravity matched the expected terrestrial force of gravity, and decreased when these cues were modified. The reduction was largest when both load- and vestibular-related cues of gravity were different from normal. Our results indicate that the vestibular drive for standing balance control is attenuated when sensory cues of gravity are not congruent to normal (i.e. terrestrial) expectations of standing balance and that the degree of attenuation is dependent upon the cumulative incongruency that arises from multiple sensory cues.
...
By monitoring head movement and orientation in space, the vestibular system can evoke appropriate muscle responses in order to maintain standing balance. The present study investigates whether vestibular-evoked muscle responses are dependent on sensory cues of gravity by examining these responses across varying load and gravity conditions. Standing subjects were exposed to a stochastic electrical vestibular stimulus (EVS, ±5 mA, 0-25 Hz) that induced a vestibular error signal, while vertical loading forces or vestibular signals of gravity were independently modified. A backboard structure limited subjects’ whole-body rotation to the sagittal plane which corresponded with the EVS-evoked sway responses in anteroposterior direction, as the subject’s head was rotated in yaw. Vestibular-evoked muscle responses were greatest when sensory cues of gravity matched the expected terrestrial force of gravity, and decreased when these cues were modified. The reduction was largest when both load- and vestibular-related cues of gravity were different from normal. Our results indicate that the vestibular drive for standing balance control is attenuated when sensory cues of gravity are not congruent to normal (i.e. terrestrial) expectations of standing balance and that the degree of attenuation is dependent upon the cumulative incongruency that arises from multiple sensory cues.
Master thesis
(2017)
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Gaia Cavallo, Jan-Willem van Wingerden, Alfred Schouten, Mark van de Ruit, Raf van de Plas, John Lataire
Human joint admittance changes with numerous factors constituting the operational point. For large changes of the operational point, joint admittance can be identified using Linear Time-Varying methods on torque and angular position signals measured on human joints. Out of the available methods, the Skirt Decomposition method was selected due to its nonparametric structure and the limited number of a priori assumptions it makes. Its employment on the identification of human joint admittance was completely novel. The method was applied to a simulation model representing joint admittance and on experimental data measured from the wrist joint. In the experiment, the subjects were changing the applied torque to follow a desired trajectory, while the angle of the wrist was perturbed by the manipulator.With a properly designed multisine input, taking into consideration the speed and complexity of the time variation, a variance accounted for (VAF) close to 100 % was obtained in the simulation study on all the tested conditions. From the experiment, it was seen that the contribution of the time variation in the frequency domain was partially masked by the output noise. The noise level could be decreased by lowering the amplitude of the desired torque, and by removing the voluntary torque from the analyzed data. With a desired torque level ranging between 5% and 20%, and considering the bandwidth between 2 Hz and 20 Hz, the mean power of the output residuals in the frequency domain ranged between 16.2 and 27.1 for all the tested conditions. Furthermore, the time-varying dynamics retrieved from the system function showed a clear correlation with the desired torque trajectory.
...
Human joint admittance changes with numerous factors constituting the operational point. For large changes of the operational point, joint admittance can be identified using Linear Time-Varying methods on torque and angular position signals measured on human joints. Out of the available methods, the Skirt Decomposition method was selected due to its nonparametric structure and the limited number of a priori assumptions it makes. Its employment on the identification of human joint admittance was completely novel. The method was applied to a simulation model representing joint admittance and on experimental data measured from the wrist joint. In the experiment, the subjects were changing the applied torque to follow a desired trajectory, while the angle of the wrist was perturbed by the manipulator.With a properly designed multisine input, taking into consideration the speed and complexity of the time variation, a variance accounted for (VAF) close to 100 % was obtained in the simulation study on all the tested conditions. From the experiment, it was seen that the contribution of the time variation in the frequency domain was partially masked by the output noise. The noise level could be decreased by lowering the amplitude of the desired torque, and by removing the voluntary torque from the analyzed data. With a desired torque level ranging between 5% and 20%, and considering the bandwidth between 2 Hz and 20 Hz, the mean power of the output residuals in the frequency domain ranged between 16.2 and 27.1 for all the tested conditions. Furthermore, the time-varying dynamics retrieved from the system function showed a clear correlation with the desired torque trajectory.