F.C.T. van der Helm
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48 records found
1
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.
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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.
Modeling and Evaluation of Cyclist Fall Prevention Interventions
A Proactive Cycling Safety Approach
How Muscle Stiffness affects Neural Control Parameters
Short-Range Stiffness Improves Stability and Feedback Robustness of Musculoskeletal Models
The study finds that the Huxley model shows positive SRS in the negative flank of the force-length curve, achieves stabilisation through only co-contraction using a lower level of required muscle excitation than both Hill-type models and stabilises both musculoskeletal systems at a larger muscle range than the Hill-type models, including in the negative stiffness flank. The feedback parameters dominantly responsible for muscle activation patterns are also more robust to change in the Huxley model. These findings suggest that intrinsic muscle stiffness impacts neural control parameters in stabilisation and reaching tasks, and further musculoskeletal modelling should consider using more complex muscle stiffness calculations for improved accuracy. ...
The study finds that the Huxley model shows positive SRS in the negative flank of the force-length curve, achieves stabilisation through only co-contraction using a lower level of required muscle excitation than both Hill-type models and stabilises both musculoskeletal systems at a larger muscle range than the Hill-type models, including in the negative stiffness flank. The feedback parameters dominantly responsible for muscle activation patterns are also more robust to change in the Huxley model. These findings suggest that intrinsic muscle stiffness impacts neural control parameters in stabilisation and reaching tasks, and further musculoskeletal modelling should consider using more complex muscle stiffness calculations for improved accuracy.
Development of the Experimental Design for the Validation of EEG Source Localization with fMRI
Application of a Haptic Robot to Identify Active Neuronal Ensembles
Activation of the somatosensory cortex is clearly visible. The visual cortex is often localized, but lacks power in some settings. Activity during the torque task can be localized, but not conclusively to the motor cortex. In all, the experiment was a success, as it was able to induce verifiable different brain states. Hypothesized task contrasts contained different activity distributions. Improvements can be made by generating a more detailed leadfield and by applying a linear manipulator ...
Activation of the somatosensory cortex is clearly visible. The visual cortex is often localized, but lacks power in some settings. Activity during the torque task can be localized, but not conclusively to the motor cortex. In all, the experiment was a success, as it was able to induce verifiable different brain states. Hypothesized task contrasts contained different activity distributions. Improvements can be made by generating a more detailed leadfield and by applying a linear manipulator
The initial phase focused on creating a smart garment with integrated textile-based electrodes named the RunWave. The second phase concentrated on analyzing muscle fatigue during dynamic running activities, employing an incremental treadmill exercise test. Fatigue was assessed using cardiorespiratory metrics and Borg's Rate of Perceived Exertion (RPE), alongside the evaluation of six fatigue metrics: Average Rectified Value (ARV), approximate and sample entropy, instantaneous mean and median frequencies, and Dimitrov's Spectral Fatigue Index. Significant differences between fatigued and non-fatigued states were observed, especially noted in shifts in entropy, mean and median frequencies, and most prominently in ARV. These findings underscored the necessity for personalized fatigue monitoring strategies, given the variation in fatigue onset and subjective exhaustion experiences among individuals.
The RunWave, with its focus on the ARV metric, emerged as particularly promising for fatigue detection. ARV's computational simplicity and interpretability make it ideal for real-world applications. Despite initial challenges such as fitment issues, electronic limitations, and garment robustness, the RunWave garment was positively received for its comfort and practicality. With targeted improvements, the RunWave garment, leveraging ARV, shows great potential for effectively monitoring muscle fatigue in runners, suggesting a substantial step forward in reducing injury risks in athletic contexts. ...
The initial phase focused on creating a smart garment with integrated textile-based electrodes named the RunWave. The second phase concentrated on analyzing muscle fatigue during dynamic running activities, employing an incremental treadmill exercise test. Fatigue was assessed using cardiorespiratory metrics and Borg's Rate of Perceived Exertion (RPE), alongside the evaluation of six fatigue metrics: Average Rectified Value (ARV), approximate and sample entropy, instantaneous mean and median frequencies, and Dimitrov's Spectral Fatigue Index. Significant differences between fatigued and non-fatigued states were observed, especially noted in shifts in entropy, mean and median frequencies, and most prominently in ARV. These findings underscored the necessity for personalized fatigue monitoring strategies, given the variation in fatigue onset and subjective exhaustion experiences among individuals.
The RunWave, with its focus on the ARV metric, emerged as particularly promising for fatigue detection. ARV's computational simplicity and interpretability make it ideal for real-world applications. Despite initial challenges such as fitment issues, electronic limitations, and garment robustness, the RunWave garment was positively received for its comfort and practicality. With targeted improvements, the RunWave garment, leveraging ARV, shows great potential for effectively monitoring muscle fatigue in runners, suggesting a substantial step forward in reducing injury risks in athletic contexts.
Methods: A robot applied continuous torque perturbations to the right wrist of the subjects. Subject were asked to perform 4 different tasks, each provoking different control strategies. Closed-loop system identification was used to estimate the joint dynamics. A neuromuscular model was then fitted to the estimated joint dynamics to express the contribution of intrinsic and reflexive pathways in physiologically relevant parameters.
Results: We show that patients are able to alter their joint dynamics in order to comply with the tasks. During the relax task patients had visibly higher admittance than controls, in the active tasks the patients were able to lower their admittance similar as controls. Patients with pathologically increased reflexes had significantly increased reflexive feedback during the force tasks compared to controls.
Conclusion: In this study we have demonstrated the ability of neuromechanical parameters to detect hyperreflexia in patients diagnosed in ALS. Therefore the proposed method of closed-loop system identification and parameters estimation could be used to monitor the progression of ALS.
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Methods: A robot applied continuous torque perturbations to the right wrist of the subjects. Subject were asked to perform 4 different tasks, each provoking different control strategies. Closed-loop system identification was used to estimate the joint dynamics. A neuromuscular model was then fitted to the estimated joint dynamics to express the contribution of intrinsic and reflexive pathways in physiologically relevant parameters.
Results: We show that patients are able to alter their joint dynamics in order to comply with the tasks. During the relax task patients had visibly higher admittance than controls, in the active tasks the patients were able to lower their admittance similar as controls. Patients with pathologically increased reflexes had significantly increased reflexive feedback during the force tasks compared to controls.
Conclusion: In this study we have demonstrated the ability of neuromechanical parameters to detect hyperreflexia in patients diagnosed in ALS. Therefore the proposed method of closed-loop system identification and parameters estimation could be used to monitor the progression of ALS.
Raw IMU data of 97 participants were used. The participants were classified as low, increased or high fall risk based on the Performance Oriented Mobility Assessment (POMA). Accelerometer and gyroscope's resultant time-series sequences (n=1037) were used as input for the Convolutional Neural Network (CNN) that was optimised and trained with 80% and tested with 20% of the participants. The results were compared with the performance of an existing portable sensor-based fall risk assessment called the Smart Floor (SF). The macro F1 of the unweighted (40%) and weighted (41%) multiclass classification CNNs was lower than the macro F1 of the SF (49%). The binary classification CNN's macro F1 (56%) was slightly lower than the SF's performance. All CNNs were better at predicting high-risk sequences. All models had poor performance when all three POMA fall risk categories should have been predicted. Adjustments to the data collection and CNN optimisation methods should be performed to study the possibility of predicting fall risk using raw IMU data in geriatric rehabilitation centres.
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Raw IMU data of 97 participants were used. The participants were classified as low, increased or high fall risk based on the Performance Oriented Mobility Assessment (POMA). Accelerometer and gyroscope's resultant time-series sequences (n=1037) were used as input for the Convolutional Neural Network (CNN) that was optimised and trained with 80% and tested with 20% of the participants. The results were compared with the performance of an existing portable sensor-based fall risk assessment called the Smart Floor (SF). The macro F1 of the unweighted (40%) and weighted (41%) multiclass classification CNNs was lower than the macro F1 of the SF (49%). The binary classification CNN's macro F1 (56%) was slightly lower than the SF's performance. All CNNs were better at predicting high-risk sequences. All models had poor performance when all three POMA fall risk categories should have been predicted. Adjustments to the data collection and CNN optimisation methods should be performed to study the possibility of predicting fall risk using raw IMU data in geriatric rehabilitation centres.
EEG Derived Laterality Metrics as a Biomarker for of Upper Limb Recovery in Stroke
An Explorative Study into EEG Biomarkers for Longitudinal Stroke Recovery
Measuring Ground Reaction forces in Running Specific Prostheses
A Fibre Optical Sensor approach
Haptic feedback in upper limb prostheses
Development and evaluation of a pneumatic haptic device providing feedback for upper limb prostheses
Aerodynamics of a skater leg
Experimental investigation on Reynolds number effects
The goal of this study is to investigate Reynolds number effects along the leg of a skater mannequin. To do so, potential drag crisis behaviour is studied via robotic Particle Image Velocimetry and Infrared Thermography for speeds ranging between 5 m/s and 25 m/s. The boundary layer state and the critical velocity distribution along the leg, based on the wake width variation, are evaluated for the bare mannequin and the mannequin wearing a skating suit optimized for ∼ 15 m/s.
Results reveal drag crisis behaviour along the knee, lower and upper leg. Furthermore, the flow topology is not only governed by the leg geometry, but also by streamwise vortices. These streamwise vortices cause an increase of the wake width below the calf and a reduced velocity deficit behind the upper leg. Most significant differences in wake width between the bare and the dressed leg are observed at 17.5 m/s. The latter observation is also supported by the Infrared Thermography results.
It can be concluded that the flow across the leg partly differs from cylinder flow, mainly because of streamwise vortices that locally affect the drag crisis behaviour.
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The goal of this study is to investigate Reynolds number effects along the leg of a skater mannequin. To do so, potential drag crisis behaviour is studied via robotic Particle Image Velocimetry and Infrared Thermography for speeds ranging between 5 m/s and 25 m/s. The boundary layer state and the critical velocity distribution along the leg, based on the wake width variation, are evaluated for the bare mannequin and the mannequin wearing a skating suit optimized for ∼ 15 m/s.
Results reveal drag crisis behaviour along the knee, lower and upper leg. Furthermore, the flow topology is not only governed by the leg geometry, but also by streamwise vortices. These streamwise vortices cause an increase of the wake width below the calf and a reduced velocity deficit behind the upper leg. Most significant differences in wake width between the bare and the dressed leg are observed at 17.5 m/s. The latter observation is also supported by the Infrared Thermography results.
It can be concluded that the flow across the leg partly differs from cylinder flow, mainly because of streamwise vortices that locally affect the drag crisis behaviour.