Effective Human-Machine Interaction (HMI) depends on accurately capturing and interpreting information from the human user. For systems relying on hand-based operation, understanding the limits of human cognitive-motor abilities is crucial to designing intuitive and efficient interfaces. This study presents an experimental setup involving four analog buttons with a minimally complex control, where human hand performance is assessed through simultaneous multi-finger Fitts' Law tasks. Initially, task difficulty was assumed to be computed by summing the individual indices of difficulty for each button, which resulted in peak throughput performance with two fingers. However, this approach did not align with Fitts' Law. By applying a weighted summation, with weights based on the variation of distances within a task, the difficulty measure better conformed to Fitts' Law, and the highest throughput was achieved with a single finger. These findings highlight the interdependence in multi-finger movement complexity and emphasize the importance of considering cognitive-motor limitations when designing HMI interfaces to optimize user performance.

Field hockey sticks are interesting hand-held sports equipment, due to their duality in preferred stick behaviour. The stick is used for striking, where a high power is desired; but also for stopping, where good control is required. This report aims to identify the properties that influence stick performance and to design a field hockey stick with adaptable properties to improve stick performance; where performance is defined as the ability of the stick to develop a high velocity when hitting a ball, and to provide proper control when stopping the ball.
The stiffness, damping and mass of the stick are properties influencing the stick behaviour; these properties are present locally, at the impact location, as well as over the full length as deflective properties due to the moment originating from the ball impact.
The deflective stiffness and damping properties are identified by applying a disturbance force on the stick tip and measuring the displacement; this shows a range of stick stiffness from 1.4 to 3.0 kN/m and a stick damping from 0.5 to 2.7 Ns/m. Measurements are performed analysing the influence of stiffness, damping, mass and effective mass; this is done by a setup where a stick falls down towards a ball and the ball distance is measured. Additionally, a mathematical model is developed for the analysis of these stick properties. This consists of a collision model, including the coefficient of restitution reflecting the stiffness and damping properties. It can be concluded that the effective stick mass is most influential and the desired properties are opposite for striking and stopping.
A design of a mechanism that fits inside the stick is proposed, this mechanism reacts to the angular acceleration of the stick, and thereby changes its properties between striking and stopping a ball. It adapts the effective mass of the stick, by two weights moving towards the head of the stick when striking a ball. By this increase in effective stick mass, an increase of 7% (compared to the original effective mass) of the ball velocity after hitting the ball is expected.

Background: Targeting systemic hemodynamic parameters in critical care settings does not always improve patient outcomes. The cellular oxygen metabolism (COMET) monitor noninvasively measures mean mitochondrial oxygen tension (mitoPO₂) and its variance via delayed fluorescence of protoporphyrin IX. However, microvascular oxygenation is often heterogeneous, allowing hypoxic and normoxic tissue to coexist, potentially leading to organ dysfunction. 

Methods: A new algorithm was developed to study underlying mitoPO₂ distributions. To evaluate performance, the algorithm was first tested on simulated unimodal and bimodal mitoPO₂ distributions across varying signal-to-noise ratios (SNRs). It was then applied to clinical data from cardiac surgery patients.

Simulation results: The algorithm accurately recovered the mean mitoPO₂  and underlying distributions across varying SNRs.

Clinical results: A total of 47 patients were included in the analysis, with 28 developing cardiac surgery-associated acute kidney injury (CSA-AKI) and 19 not. The mitoPO₂ calculated by the developed algorithm generally followed the results from the COMET closely but showed overestimation in the lower oxygen range. The CSA-AKI group spent significantly more intraoperative time with mitoPO₂ <25mmHg consistent with previous research.

Conclusion: The developed algorithm proved to be reliable in estimating the mean mitoPO₂. Larger patient cohorts are needed to validate the role of the algorithm in CSA-AKI risk assessment.

Note: title and abstract differ from original due to confidentiality.

Background
Rheumatoid arthritis (RA) is a progressive, chronic, inflammatory, autoimmune disorder, affecting 0.5-1.5% of the world's population. Current RA disease activity assessment methods rely on subjective, labor-intensive methods. Therefore, an unmet need exists for objectively assessing RA disease activity through a low-labor method. It is hypothesized that a correlation exists between passive MCP joint stiffness and RA disease activity.

Objective
The objective of this study is to design, develop, and evaluate a measurement tool that can assess passive MCP joint stiffness of the index-, middle-, and ring fingers of both hands objectively. The tool will be evaluated on healthy participants.

Design methods
A structured approach is adopted for the design of the measurement tool. The design challenges are mapped out by writing down the requirements into four categories. In the next design phase, the partial functions and their corresponding potential solutions are worked out. Three concepts are developed which combine different sets of potential solutions. The most promising concept is determined through a multicriteria analysis and worked out into a final design. Before the evaluation process commences, the final design is reflected on and improved where necessary.

Evaluation methods
Firstly, the tool is verified by subjecting it to a linear torsion spring rather than a human MCP joint. Secondly, the passive resistance behavior of a human MCP joint is analyzed during a full cycle of flexing and moving back to the initial position. Thirdly, the tool is used for an inter-day evaluation to determine the precision of the tool when used on human MCP joints (expressed as coefficient of variation (CV)). Finally, a repetition test is performed to determine the influence of setup-related causes on the precision of the tool.

Results
The linear spring evaluation showed that the mechanical system of the tool is very precise, exhibiting a coefficient of variation of 0.6%. The continuous cycle evaluation showed that the passive resistance of the MCP joint behaves nonlinearly and that the MCP joints have viscoelastic properties. The calculated CV for the inter-day precision evaluation is equal to 33.0%. The CVs for the repetition tests were equal to 13.4% and 27.7% for the fixed and non-fixed approaches respectively. Other evaluated aspects are the weight of the tool (1.6 kg), volume of the tool (10.8 dm³), comfort for the user (9 out of 10), and full six-joint assessment time (7.5 min).

Discussion
The CV of 33.0% for the inter-day precision evaluation is too large to subject the tool to RA patients. However, the repetition test showed when the hand under assessment is consistently aligned and orientated, the system is much more precise, exhibiting a CV of 13.4%. So, when design components are included that assist with the alignment and orientation of the hand under assessment, the tool can be subjected to actual RA patients to evaluate the increase in passive MCP joint stiffness during (the early stages of) inflammation.

During a brain tumour resection, a neurosurgeon is constantly navigating a delicate balance between resecting as much of the tumour as possible, while avoiding any damage to healthy brain tissue. This challenge is particularly difficult when the tumour is located in a critical functional area, involved in for example language or motor function. For these types of tumours, the awake craniotomy was developed. During this surgery the patient wakes up to perform language and motor tasks, to enable the surgeon to localize these functions inside the brain. In this thesis, we investigate and develop a new quantitative method to monitoring motor function that could potentially improve intraoperative decision making and enables neuroscientific and neurosurgical research.

Chapter 1 provides a background about surgical strategies and technologies that have been developed to aid surgeons’ decisions during complex brain tumour resections. We will explain the complexity of robust research in the neurosurgical environment and the need for a dedicated Research Operating Room to create an environment to improve neurosurgical and neuroscientific research.

In Chapter 2 we make an overview of the possible solutions to quantify motor function before, during and after awake craniotomies and discuss the best solution for the Erasmus MC.

In Chapter 3 we present a new frame to create a standardized environment inside the operating room for good quality data collection of patient functionality. To design this frame, we identified and interviewed all the important stakeholders and designed three prototypes. The two most promising prototypes were developed. The final prototype was implemented during three awake craniotomies.

This newly developed frame was used in Chapter 4 to explore video tracking as a new tool to quantify hand motor function. Three patients were followed one day prior to the surgery, during the awake craniotomy, and one day postoperatively. During these three cases, we identified several prerequisites for a reliable recording set-up and explored the potential to detect clinically relevant events during fingertapping and direct electrical stimulation (DES). This showed promising results and underscores the potential for video tracking to be further investigated for quantification of hand motor function.

In Chapter 5 we put the discussed work into context, discussing it’s clinical and scientific relevance and future perspectives. In this thesis, we have demonstrated that it is possible to implement a new quantitative measurement method to monitor hand function in the challenging environment of an operating room. Quantification of visual observations has shown to be low-cost, easily available and implementable in clinical context, because of the fast technological advancements in this field. Video tracking can be used for future research to investigate the relation between intraoperative findings and long-term outcomes, and has the potential to add valuable information for neurosurgical and neuroscientific research.

Robotic rehabilitation systems that provide proprioceptive information offer a promising approach to helping stroke survivors regain lost proprioceptive functions. One potential way to provide proprioceptive information is viscosity rendering. However, how the modulated viscosity rendering affects brain activity remains unclear. To investigate the correlation between viscosity rendering and brain activity and to provide a neurological basis for the design of robotic rehabilitation systems, an experimental setup was developed to deliver various viscosity rendering and fixed stiffness rendering during hand movement. In the experiment, twelve healthy participants interacted with virtual bottles with the same bottle stiffness but filled with liquids of different viscosities under the same movement speed, providing different levels of proprioceptive information in the form of force on muscles and joints. Control conditions without viscosity and stiffness rendering were also tested, involving both passive and active hand movements at the same speed. Results showed that stronger mu-ERD and beta-ERD were observed during movements with viscosity and stiffness rendering compared to control conditions. No significant evidence suggested that different viscosity in rendering caused variations in mu-ERD or beta-ERD. Additionally, no significant differences were found between active movement without haptic rendering and passive movement in EEG activities. These findings suggest that while the existence of viscosity and stiffness rendering during movement strengthens brain activity, modulating viscosity rendering does not significantly affect this response. This insight is particularly valuable for designing robotic rehabilitation systems that incorporate viscosity rendering.

Preventive, personalized treatment of biopsychosocial (BPS) functional decline or stagnation in the rehabilitation process requires early prediction of those at risk. Ambulant monitoring of objective physical activity using wearable accelerometers could be a quantitative, noninvasive, and affordable method to gain insight into current and future biopsychosocial functioning.

In this retrospective study, Random Forest Regression was used to predict cross-sectional and longitudinal Functional Ambulation Category (FAC) and Six-item Cognitive Impairment Test (6CIT) after hip fracture using ambulatory accelerometry data. Accelerometry data and BPS functional assessments were available of 49 participants of the HIPCARE study, assessing prognostic determinants of outcome after hip fracture in the elderly.

Overall, cross-sectional FAC scores three months after hip fracture could be predicted with moderately low error, and categorized regression predictions showed high precision and recall. Cross-sectional 6CIT and both longitudinal regression models underperformed, but categorized regression predictions revealed mixed but more promising precision and recall.

It is expected that the predictive performance of models can be improved by increasing participant sample size with balanced samples over population-specific, prevalent ranges of BPS outcome scales and exploring additional machine learning models. In the future, accurate accelerometry-based predictions for individual patients needing rehabilitation could support personalized treatment and improve long-term biopsychosocial functioning.

Feedback error learning (FEL) is a classical computational model that describes human motor learning. It consists of forward and inverse models representing internal dynamics and environmental disturbances. Such models can be used as controllers that represent the function of the motor cortex. On top of FEL, a model has been built with jointly trained feedforward and feedback controllers using a neural network. The controllers that actuated six muscles driving a two-degrees-of-freedom arm model were trained offline. This model successfully simulated human learning of point-to-point reaching movements in a horizontal plane when it was tasked to adapt itself in a null field (NF) and a velocity-dependent force field (VF). In this study, we further tested this model in the divergent force fields (DF) and a channelled force field (CF) to observe its performance. The comparison between the simulation results and the experimental evidence suggests that this model can predict some of the key features of the learning process, such as the kinematics, the muscle dynamics, and the impedance profiles. The learning decay was hindered when the lateral error was artificially eliminated by the CF, as reported in the literature. Overall, the model could converge towards realistic

The human brain, with its intricate web of billions of neurons and trillions of synaptic connections, is a remarkable organ responsible for performing complex cognitive processes. While brain imaging techniques like fMRI and EEG provide insights into neural activity, there is no broadly accepted mathematical framework for the collaborative activity of neuronal populations and their communication. In the interdisciplinary fields of neuroscience and computational modelling, this research introduces a data-driven mathematical framework for modelling the brain's cortical network, derived from EEG data, while incorporating an exogenous input.

This framework captures brain dynamics, forming a state-space model using subspace identification, and evaluates statistical dependencies among brain sources, known as functional connectivity. Employed on a dataset mimicking a sensorimotor task, it proves effective in characterizing brain dynamics, with the PO-MOESP method outperforming N4SID. Additionally, the framework is competent to assess functional connectivity between brain sources, resulting in a network connectivity diagram that offers valuable insights into the statistical relationships between distinct brain regions. Altogether, it is concluded the designed algorithm can determine the intricate interactions within the brain during a simple passively performed task stimulating the brain. Consequently, this achievement forms a robust foundation for advancing Brain-Computer Interfaces (BCIs) and enhancing the diagnosis of neurological disorders.

We propose a novel shared control interface that enables teleoperated teaching of both high-level decision-making skills and low-level impedance modulation skills using a single haptic device. In the proposed method, high-level teaching is achieved by repurposing the haptic device to remotely modify Behaviour Trees (BTs), allowing human operators to guide decision-making. Repurposing of the haptic device is achieved by exploiting its degrees of freedom for different functionalities. Low-level skill teaching involves an impedance command interface, that is used to command endpoint stiffness by manipulating a 3D virtual stiffness ellipsoid with the haptic device. Both teaching modes are connected: a newly demonstrated low-level skill appears in the BT at a user-specified index. Control is shared between the human and the autonomous system on a high- and low-level. At the higher level, the human can change the BT online, while ongoing execution of the low-level actions within behavior tree remains uninterrupted. During low-level teaching, shared control is implemented between the robotic motion skill and human-demonstrated stiffness. To provide a proof-of-concept and demonstrate the main features of the proposed interface, we performed several experiments in a teleoperation setup operating a remote shelf-stocker robot in a supermarket environment. A predefined BT encodes high-level decisions for a pick-and-place task. The impedance command interface is evaluated in a “peg-in-hole”-like task of placing a product on a cluttered shelf. Ultimately, the proposed interface can facilitate teleoperation-based Learning from Demonstration for the transfer of both high- and low-level skills in an integrated manner.

Introduction. Needle electromyography (EMG) is a diagnostic tool used to identify and localise neuromuscular disorders, but the current evaluation by a neurologist is subjective and requires years of training. Artificial intelligence can be used to automatically classify needle EMG signals. However, previous studies in this area have been subject to bias and have overestimated their performance. As a result, we aim to develop a clinically applicable method for automatically evaluating needle EMG signals to provide a more objective and efficient means of analysis. Methods. In this study, we implemented and evaluated two unsupervised learning models, a convolutional autoencoder + k-means clustering model and a deep convolutional embedded clustering model. The models were evaluated on two classification tasks: the classification of spontaneous, voluntary and insertional activity and the classification of spontaneous activity in needle EMG data classified as rest. We used hospital-acquired needle EMG data from the Amsterdam University Medical Centre (UMC), location AMC, from a total of 326 patients. The data was converted to Mel spectrograms, resulting in a total of 1.3 million images available for training. Results. The unsupervised learning models reached 93.5% accuracy on unseen test data. The classification of phenomena present in rest, such as fibrillation potentials and positive sharp waves, was less successful and requires further research. Conclusion. Our study provides valuable insights for the use of unsupervised learning in the automatic classification of needle EMG signals and highlights the need for further research in this area.

Advanced Driver Assistance Systems (ADAS) aim to increase safety and reduce mental workload. However, the gap in the understanding of the closed-loop driver-vehicle interaction often leads to reduced user acceptance. In this research, an optimal torque control law is calculated online in the Model Predictive Control (MPC) framework to guarantee continuous guidance during the steering task. The novelty lies in the integration of an extensive driver-in-the-loop model within the MPC-based haptic controller to enhance collaboration. The driver model is composed of a preview cognitive strategy based on a Linear-Quadratic-Gaussian, sensory organs, and neuromuscular dynamics, including muscle co-activation and reflex action. Moreover, an adaptive cost-function algorithm enables dynamic allocation of the control authority. Experimental data was gathered from 19 participants in a fixed-base driving simulator at Toyota Motor Europe, evaluating an MPC controller with two different cost functions against a commercial Lane Keeping Assist (LKA) system as an industry benchmark. The results demonstrate that the proposed controller fosters symbiotic driving and reduces driver-vehicle conflicts with respect to a state-of-the-art commercial system, both subjectively and objectively, while still improving path-tracking performance. Summarising, this study tackles the need to blend human and ADAS control, demonstrating the validity of the proposed strategy.

System identification techniques to analyse movement disorders are in development, but concrete clinical evidence to receive broad support from the clinical world is lacking. This master thesis proposes two ways to accelerate their development as part of the master’s programmes in Mechanical Engineering and Science Communication. The Mechanical Engineering part focuses on the development of system identification techniques themselves. For this study, an ensemble-based algorithm is developed to identify intrinsic and reflexive joint stiffness during voluntary movements. The algorithm combines two methods from previous research: the Parallel-Cascade method to separate intrinsic and reflexive stiffness and an instrumental variable approach to allow subjects to carry out voluntary movements and apply closed-loop identification. A simulation and experimental study are conducted with the human wrist joint in which a sinusoidal angle reference trajectory is followed using multiple realisations of the same trial. To elicit the modulation of intrinsic parameters, a torque field is applied that is linearly dependent on the wrist angle with a greater flexion resulting in a higher torque. In the simulation study, the intrinsic and reflexive pathways are separated and over 98% of the variance is explained. In the experimental study, the modulation of intrinsic stiffness is estimated with an explained variance of over 90%. Intrinsic stiffness is highest during the peaks of the trajectory and lower during the transition phase from one peak to another. However, the torque response of the experimental study shows that no significant extra torque is added during the reflexive EMG peak. The obtained results underline the promising potential of the algorithm and open up new possibilities once the algorithm is tested on an experimental task where more reflexes are elicited. The Science Communication part focuses on developing a shared mental model to transfer knowledge on the processes behind cross-disciplinary collaborations to mechanical engineers, as devoting time and effort to the metacognitive process helps in coping with it. However, most theories on cross-disciplinarity are written from a social sciences paradigm and are inherently difficult to interpret from a mechanical engineering paradigm. An analogy is developed to link concepts from both epistemologies together. This is done by assembling a theoretical framework containing concepts on the processes behind cross-disciplinary collaborations. These concepts are linked to analogous counterparts from mechanical engineering by relying on conceptual similarities between mechanical stiffness and collaborative pliability. As a result, knowledge of cross-disciplinary collaborations can be immersed with existing conceptual knowledge of mechanical stiffness. Collaboration theories become part of the primary discipline instead of being treated as a separate discipline. The model, called the Collaborative Pliability Model, is assessed by a group of six mechanical engineering students. The students considered the model to be an effective tool for learning about cross-disciplinary collaborations. By using the model, they came up with numerous new factors affecting the success of their collaborative process. This gives a promising outlook for the future and opens the door for new applications for developing an analogy used to transfer knowledge between specific target groups.

Reflexes and co-contraction are the two mechanisms used for effective limb control when humans face unexpected perturbations in their daily activities. When the environment has reduced stability margins, reflexes are tempered due to the oscillations caused by the neural time delay of the reflexive pathways. An explanation is that reflexes adapt to the dynamics of the environment and stability margins are the constraint. This view requires that humans assess stability margins, which could happen by detecting changes (i.e. oscillations) in the perturbation eliciting the reflexes. The perturbation perceived by the human is the actual perturbation filtered by the dynamics of the environment. Therefore, changes in the stability of the environment influence the perceived perturbation. The goal of this study is to determine whether reflex modulation is triggered by the environmental dynamics (i.e. damping and stability margins) or by the properties of the perturbation eliciting a reflexive response. An experiment was designed where participants were asked to minimize the displacements caused by continuous force perturbations applied to the hand while interacting with different environmental dynamics. Some of the perturbations were prefiltered to mimic the filtering effect of the environmental dynamics. Variations in the dynamics of the shoulder joint were quantified through the estimated arm admittance (i.e. displacements in response to force perturbations). The results show variations in the admittance between the perturbations mimicking the environment and the true environment at low frequencies (below 5Hz); and for different prior knowledge about the environmental dynamics (below 2Hz). These variations indicate that perturbations can be designed to mimic the environmental dynamics, and that perturbation properties and stability constraints cause changes in the motor behaviour.

Autonomous driving is a development that has gained a lot of attention lately, because it can lead to major improvements in the mobility sector. One example of a research project that aims to develop vehicles that are capable of reaching the highest level of autonomy in driving, is the WEpods project. The goal of this research is in line with this aim, having the thesis objective defined as follows: let the WEpods continue driving in autonomous mode more often than is currently the case.

The WEpod shuttles are not yet completely able to drive autonomously due to their inability to handle unexpected behavior (terminology: faults). Currently, such faults need to be detected and solved by a steward, who will manually initiate a safe stop if necessary. The localization module, which is responsible for localizing the vehicle on a map, sometimes generates unreliable location estimates. This poses two challenges. First, the fact that there is a mismatch between reality and the sensor outcomes of the localization module that needs to be detected. Second, the question of how to prevent the system from showing behavior that is different from what is desired (terminology: failure) in case such a fault is present (terminology: fault tolerant contol). Fault tolerant control can be performed in either a passive or an active manner. The passive approach ensures that either the faults are prevented or the system is able to mitigate them by anticipation in the design. The approach evolves from passive to active fault tolerant control when an on-line adaptation of the system control is made. For applications in autonomous driving, it is apparent that it is important to handle not only anticipated faults, but also to be able to deal with unexpected faults in an on-line manner. This on-line fault tolerant control approach involves two fault diagnosis steps that lead to solving the first challenge: detection and isolation.

A so-called model-based fault diagnosis approach turned out to be most suitable, as it has been used for similar applications in the past. However, a model-based fault diagnosis approach has not yet been implemented for detecting and isolating faults in a localization module of autonomous driving, indicating the scientific relevance of this research. In the model-based approach, kinematic and dynamic equations of the research vehicle (WEpod) are used to build a computational model. This model is then subjected to an observer, that is able to compare the model outcomes with the actual measurements in an off-line way. A residual is drawn up by taking the difference between the model outcomes and the measurements. A threshold is computed based on noise on the measurements to compare the residual with. When the residual exceeds the threshold, an alarm is raised. This way, the system itself has been enabled to detect faults when they occur internally.

Inclusion of the suggested fault diagnosis approach in an on-line manner into the system is a big step towards fully autonomous driving of the WEpods, and therefore the goal of this research is met.

Fascicle shear is theoretically a mechanism by which skeletal muscles lengthen. Existing ultrasound based techniques allow measurement of muscle architecture parameters, but not the quantification of shear strain. In this study, an algorithm that tracks the shear strain from ultrasound images using a continuum representation of the muscle was developed. Errors in strain tracking may arise due to misalignment of the imaging plane, movement artifacts and non-uniform strain within the muscle. The goal of this study was to develop and validate the newly developed strain tracking algorithm. Various potential sources of error are investigated. A computer model was created consisting of a three-dimensional (3D) synthetic volume, representing the muscle. Virtual ultrasound images were then sampled from the 3D volume by intersecting the synthetic volume under a known angle of the imaging plane. The measurement error was defined as the difference between the known strain that was imposed virtually to the 3D muscle volume and the strain calculated with the tracking algorithm. The measurement error was determined for conditions of combined axial and shear strain, plane misalignment, plane rotation and non-uniform strain. Conditions were simulated between 30 and 100 times, each time with a different synthetic muscle volume.
The developed strain tracking algorithm provided strain measurement of sub-pixel accuracy and precision when the imaging plane was aligned with the fascicles. Rotation of the ultrasound transducer relative to the muscle resulted in invalid measurements. Axial strain was overestimated when the muscle exhibited a non-uniform axial strain pattern. Largest errors (underestimation of strain by up to 65%) were caused by misalignment of the imaging plane with the fascicles. The large effect of misalignment emphasizes the need for careful transducer placement that requires anatomical information about the muscle structure. Strain tracking methods based on three-dimensional avoid the need for alignment, potentially allowing more accurate measurement of strain.

Pathological tremor is currently not adequately treated with medication; it is effective in only 50% of cases and often does not supress tremor more than 60% at the cost of mild to severe side effects. In recent years, mechanical tremor suppression concepts have been developed, but none have thus far been implemented as replacements for current medication. This paper presents a proof of concept adaptive damper for tremor suppression. Results show that the prototype is able to attain a torque difference of 0.05Nm. Furthermore, the results showed that the prototype was capable of varying the damping torque for the same frequency. Finally, experiments showed that the prototype was able to differentiate between frequencies, meaning that the prototype provided a damping torque to an input perturbation only when a pre-selected frequency was present. Future efforts should be focussed towards optimization of components to increase the maximum torque difference. A working orthosis based on this concept could bring us one step closer to providing a stable life for tremor patients around the world.

Amyotrophic lateral sclerosis (ALS) is themost frequent form of motor neuron diseases (MND). This neurodegenerativedisease progresses relentlessly quick. The characteristic feature of ALS is theconcurrent degeneration of the upper and lower motoneurons (UMN & LMN) inthe central and peripheral nervous system. Symptomatic behavior in ALS is theresult of complex symptom interplay, as well as symptom counteraction. Therefore,adequate examination of motor function in ALS requires an examination techniquethat quantifies disease symptoms at their origin, rather than at their point ofexpression. The goal of this study was to quantify and explore the effect ofALS on intrinsic and reflexive properties of the limbs of the patients underpassive and active conditions. To the best of our knowledge, this study was thefirst to assess a MND within this particular framework. A group of10 ALS patients and 9 controls were recruited for participation in an extensiveprotocol, comprising of several motor tasks and maximal voluntary contractionmeasurements. The tasks were performed on a single-axis wrist manipulator,which produced multisine torque perturbations. A linear system identificationand parameter estimation procedure was implemented to estimate the parametersof a neuromuscular model with 5 task-dependent (2 intrinsic, 3 reflexive) and 8task-independent parameters. Allparticipants were able to fully comply with the study protocol and indicatedthat the effort required for the tasks was easily maintained. Variance of theintrinsic parameters was generally increased in the patient group, with atendency towards a reduced median. The median of the reflexive parameter musclespindle position dependence was significantly increased in ALS patients during arelax task. Additionally, reflexive and intrinsic parameters of individual patientswere frequently found to be responsive to the patients respective clinicalstate. Muscle stiffness during the relax task was strongly correlated to thesummed average EMG in ALS patients (r = 0.75, p = 0.013). During active tasks,three main control strategies were derived from the intrinsic and reflexiveparameters in patients. We showthat our study protocol is viable for examination of ALS patients within alarge range of physical capacity. The use of quantitative parameters allowed usto disentangle fundamentally overlapping UMN and LMN symptoms. The derivedparameters were shown to enhance clinical scores within their respective clinicaldefinitions. Lastly, our results offer new insights beyond the current clinicalknowledge of motor function under active conditions in ALS.

Half of the long-termed disabled stroke survivors experience increased hyper-resistance of the wrist. Discrimination between the two components of joint hyper-resistance, i.e. the neural reflexive and intrinsic tissue component, is important since the components require a different treatment method. To discriminate between the two components, objective methods are developed that make use of bio-mechanical modeling. This research aimed to address the agreement between a clinically easy applicable modeling method, the NeuroFlexor method, and a more comprehensive optimization method, which both objectively obtain the neural and intrinsic components of joint hyper-resistance. Furthermore, this research study addressed the agreement between the neural and intrinsic components obtained with the NeuroFlexor and ptimization method, and the external validation of the two components with clinical rating scales.
Method NeuroFlexor based assessments and instrumented positional wrist perturbations were applied to chronic stroke survivors (n = 49) and healthy volunteers (n = 11). The neural and intrinsic components were estimated using the NeuroFlexor method, whose method is a force-relationship method, and a nonlinear electromyography driven wrist optimization model. The Modied Ashworth scale (MAS) was rated to all stroke survivors as clinical scale. Correlation analysis was conducted to nd the agreement between the components of both methods, and to nd the agreement between the MAS and the components. To analyze how well the neural and intrinsic components of both methods were able to predict the MAS, multiple regression analysis with a backward selection procedure was conducted for both methods separately and both methods together. On the healthy subjects, the optimization model was applied on the NeuroFlexor data to check differences in model structure.
Results The neural components of both the NeuroFlexor method and optimization method had a strong correlation (r = 0.656), as well as the intrinsic components (r = 0.648). For both methods, the neural and intrinsic components were signicant estimators of the MAS, and the NeuroFlexor method and the optimization method were approximately equivalently able to predict the MAS (r^2 = 0.466 and r^2 = 0.519, respectively). For the multiple regression analysis with the intrinsic and neural component of both methods together, the neural component of the optimization method together with the intrinsic component of the NeuroFlexor method were more able to describe the MAS (r^2 = 0.605) than the two components of the methods separately. For healthy patients, the optimization model was not able to reliably estimate the two components from the NeuroFlexor data.
Conclusion This study found evidence to support the use of the NeuroFlexor device for quantication of the neural and intrinsic components of wrist hyper-resistance post stroke. Further research is needed to establish the validity of the neural component of the NeuroFlexor and the intrinsic component of the optimization method.