Objective Action tremors affect many Parkinson’s disease (PD) patients and can significantly impair daily functioning. These tremors often persist despite dopaminergic therapy, while stereotactic surgery, though effective, is invasive and not suitable for all. Alternative treatment options are therefore needed. The STIL Orthosis is a wearable device developed to reduce forearm tremor. Its effectiveness has been shown in essential tremor, but its impact on PD-related action tremors remains unknown. This pilot study evaluates its effect in a small PD sample.

Methods: Three PD patients with action tremor participated in a sham-controlled, single-blind, crossover pilot study. Tremor severity was assessed in three conditions: baseline, sham, and STIL Orthosis. The primary outcome was the TETRAS score. Secondary outcomes included MDS-UPDRS tremor scores, tremor power (accelerometry and sEMG), and patient satisfaction (PGI-I).

Results: Although effects did not reach statistical significance (for n=3), a consistent trend toward tremor reduction was observed across clinical and accelerometry-based measures when comparing the STIL Orthosis to baseline. TETRAS scores decreased from baseline \(13 \pm 4.0\) and sham \(11 \pm 5.2\) to orthosis \(8.0 \pm 2.1\). MDS-UPDRS tremor scores similarly declined (baseline: \(7.3 \pm 0.9\), sham: \(5.3 \pm 1.7\), orthosis: \(3.3 \pm 0.9\)). Two participants showed reduced tremor power (up to \(55\%\) compared to baseline). EMG tremor power of the ECR and FCR muscles decreased by \(15\%\) and \(58\%\), respectively. All three participants reported slight improvement with the orthosis (PGI-I).

Conclusion: This pilot study indicates that the STIL Orthosis influences action tremor in PD. Larger studies are needed to assess its clinical significance.

The influence of intrinsic properties (mass, stiffness, and alignment) on ankle joint dynamics, along with the sensitivity of a parametric optimization model to changes in these properties, was investigated. Experiments were performed on two young adults using a robotic manipulator to apply controlled perturbations comprising multiple ramp-and-holds while recording position, torque, and electromyography (EMG) data.
Position, torque, and EMG data were subsequently used as input for the computational model, producing a modeled torque. The model, designed to parametrize neural and non-neural contributors to joint stiffness associated with spasticity, employs a 23-parameter Hill-type neuromuscular framework to estimate muscle properties by minimizing the difference between modeled and measured torque.
Experimental results revealed that mass and stiffness significantly influenced the required torque, particularly during the hold phases, while alignment had minimal effects. Model sensitivity analysis showed similar trends, with stiffness having the greatest impact on torque, followed by mass. Changes in alignment were minimal, highlighting its redundancy.
Discrepancies were observed between model predictions and experimental data in capturing the effects of mass and stiffness changes. The model predicted equivalent mass effects regardless of angle, whereas experimental data exhibited angle dependency. Furthermore, modifying stiffness did not affect the dynamic phases of the modeled torque but did influence the dynamic phases of the measured torque.
These findings highlight the complex interplay between biomechanical factors and torque generation. Future work should refine the model to capture physical nuances better, improve experimental setups to control joint angles and optimize model parameters in a staged approach. Such efforts will advance the development of the model and deepen the understanding of spasticity, paving the way for more accurate and personalized treatment strategies.

Work-related musculoskeletal disorders (WRMSDs) are prevalent among sonographers, particularly affecting the shoulder region due to repetitive and static movements. This study introduced an ergonomic posture and a mobile arm support (MAS) to reduce the load of muscles contributing to the stabilization of the shoulder. The experimental setup replicated a conventional cardiac ultrasound examination, using a phantom model and simulations to mimic cardiac ultrasound procedures. Professional cardiac sonographers were instructed to acquire three cardiac views (parasternal long-axis view (PLAX), apical four-chamber view (A4C), and subcostal window(SCW)) while surface EMG and joint angles of the shoulder, elbow, and back were measured. Additionally, subjects were tasked with completing a questionnaire to gather subjective outcomes of usability and satisfaction with the support and ergonomic posture. Muscular activity of the middle deltoid muscular activity (PLAX; F(3,12)10.15,p=.001,η2 p=.717, A4C; F(3,12)=5.75,p=.011,η2 p=.590) and superior trapezius (PLAX; F(3,12)=7.05,p=.005,η2 p=.638) decreased significantly during examinations with postural changes but increased significantly while only support was provided. The support was considered slightly useful (SUS=65.0±6.9, α=.585), but the ergonomic change was considered poor (SUS=43.0.0±14.4, α=.813). The increase in muscular activity was likely caused by incorrect placement of the MAS on the upper extremity and incorrect levels of support. Besides, sonographers reported a MAS would be useful when there is an additional functionality that applies contact force through the MAS. The addition of support to postural changes did not significantly reduce muscular load compared to examinations with only postural changes. Therefore, ergonomic postural change is a sufficient solution for mitigating WRMSDs development due to the only significant decrease in muscular activity.

Stable upper limb movements are essential for tasks, and the sensorimotor system utilizes the stretch reflex and voluntary control to counter unexpected mechanical perturbations, ensuring skilled motor behaviour. The stretch reflex is composed of two distinctive components, the short-latency (SLR) and long-latency (LLR) responses. Given their unique modulation and the mysterious neural circuitry underlying them, much remains to be discovered about LLRs. The task dependency of the long-latency response has been a focus of research since its discovery, though further exploration of the impact of a broader range of instruction characteristics is still needed. To tackle this gap in LLR research, this study investigated how changes in instruction modality and coherence of instruction direction and location influenced the LLR amplitude. An experimental paradigm with two identical tasks was designed, where subjects were instructed to reach a target visually or audibly. Furthermore, both logical and conflicting instructions were used. Mechanical countering perturbations were applied to the subjects, eliciting stretch reflexes. It is hypothesized that the LLR response amplitude estimate will show dependency on the varying instruction characteristics. The results revealed new insights into the relationship between LLRs and their dependence on task instructions. LLR amplitude estimations revealed differences between modalities. The effect of instruction coherence was not present in both modalities. The findings of this study suggest that changes in response time between modalities lead to discrepancies in LLR amplitudes. Also, the effect instruction logic and clarity have on subject confidence influences reaction times and intent, leading to differences in LLR strength. The differences in neural pathways that localize, recognize and then transform stimuli to perceptual information are seen as new potential contributors to the long-latency stretch component.

The late stage of Parkinson’s disease (PD) is accompanied by unpredictable motor fluctuations which cannot be treated sufficiently with a medication schedule with fixed time intervals. At this point, feedback-based medication timing based on the medication state would satisfy the needs of persons with PD. Wearable sensors offer the ability to objectively and continuously monitor PD-related features in an unsupervised way during daily life. Sensor fusion of a shank-worn accelerometer (Cue2walk wearable device) and a heart rate sensor enables comprehensive state monitoring through the integration of physical and physiological parameters. This study aimed to identify features, retrieved from the Cue2walk wearable device and/or a heart rate sensor, correlated with the medication state in PD, in an unsupervised way during daily life. Triaxial accelerometer and heart rate data were collected from nine persons with PD for seven days. Features in the time and frequency domain were extracted and a value was assigned to each stride. The frequency features were calculated by including walking bouts of at least 10 s. The feature values were averaged over seven block sizes. The medication state was modelled by assuming a sinusoidal form and was based on the medication intake schedule. For each averaging block size, the correlation between the features and the medication state was calculated. Due to a hardware problem, only data from one participant could be included. Stride time variability and displacement, velocity, acceleration, and covariance features resulted in the largest significant correlation with the medication state in this participant. The reliability of the correlations was limited by the short walking bout duration in the calculation of the frequency features and by the simplified model of the medication state. Therefore, future research should collect data on a longer term and include walking bouts of 25 s in the calculation of the frequency features. Moreover, medication intake should be logged by the participants. Consequently, personalised digital biomarkers for medication state monitoring can be developed in an unsupervised way, by taking into account multicollinearity.

Background
Spasticity is a condition that affects patients who have sustained an upper motor neuron lesion. Such lesions include for example cerebral palsy (CP), of which 35% is affected by spasticity, and stroke, of which 90% is affected by spasticity. Pharmacological therapy often involves the prescription of oral baclofen. However, due to the difficulty of medication in crossing the blood-brain barrier, high dosages of oral baclofen are required to achieve the desired therapeutic effect, which in turn leads to an increase of the incidence of negative side-effects. For patients with severe spasticity, the intrathecal baclofen (ITB) pump is indicated for more effective drug delivery. To monitor the efficacy of the treatment and quantify the level of spasticity, the Modified Ashworth Scale (MAS) is employed. Nevertheless, the moderate inter- and intra-rater reliability indicates that the subjectivity of the approach represents a potential limitation. Therefore, a more objective approach is required. Surface electromyography (sEMG) can measure direct muscle activity and is an objective and non-invasive approach. Yet, there is no research available on sEMG measurements to assess the change in muscle activity as a result of ITB treatment.

Objective
The primary objective of this exploratory study is to assess the effect of intrathecal baclofen on lower limb muscle activity using sEMG. The secondary objectives are assessing the correlation between the sEMG feature values and MAS scores, and between the sEMG feature values and Patient's Global Impression of Change (PGIC) scores.

Method
sEMG measurements were performed during standard assessment of the MAS on patients receiving an ITB single shot trial (SS) and/or pump implantation. The following features were analyzed: (1) Root Mean Square (RMS), (2) Peak Amplitude Value (PAV), (3) Median Frequency, and (4) Co-Contraction (CCR). A comparative analysis was conducted to assess the change in mean feature value of each individual muscle between pre- and post-treatment for the purpose of study aim 1. The Spearman's rank correlation coefficient was calculated in order to assess the relationship between changes in feature values and changes in MAS scores. Furthermore, the Spearman's rank correlation coefficient was calculated to assess the relationship between the changes in most effective feature and the PGIC scores.

Results
A total of twelve patients were included in the study, diagnosed with a variety of conditions including spinal cord injury (SCI), multiple sclerosis (MS), traumatic brain injury (TBI) and CP. Following ITB treatment, the RMS demonstrated a statistically significant decrease for three out of eight muscles: the left semitendinosus (p = 0.020), the right rectus femoris (p = 0.025) and the right tibialis anterior (p = 0.002). The PAV presented a decrease in 3-8 out of 10 patients following SS baclofen treatment. The changes in median frequency exhibited considerable variability between patients following ITB treatment. The CCR decreased in six out of ten patients for the left leg following SS baclofen treatment. A significant correlation was observed between the change in MAS scores and change in RMS in the medial gastrocnemius (p = 0.027) and the change in median frequency in the semitendinosus (p = 0.002). A low correlation was observed between the change in RMS and the PGIC score.

Conclusion
In conclusion, this study demonstrates the potential of sEMG features, such as RMS, in assessing the impact of ITB treatment on muscle activity. Future work could validate these findings by increasing the sample size and improving certain methodological aspects.

This study introduces an approach to optimizing large-scale embodied spiking neural networks (SNNs) for simulating the brain in a closed-loop environment, crucial for validating theoretical neuroscience hypotheses about the brain-body relationship. Accurately modeling this relationship at scale allows for the simulation of neural plasticity, temporal dynamics, and spike timing. Traditional parameter tuning methods are impractical for complex SNNs due to their non-differentiable nature and computational challenges. To address these issues, we apply gradient descent optimization with forward propagation through time, enhanced by surrogate gradient techniques, enabling efficient and scalable SNN tuning. We demonstrate this approach with a proof-of-concept system comprising a three-layer leaky-integrate- and-fire neural network with recurrent connections, integrated with a 2D musculoskeletal model using Hill-type muscle representations. All components are fully differentiable, allowing for gradient calcula- tions through the system. The results demonstrate that the weights are updated and the performance of the embodied SNN increases as it learns to stabilize the arms angle to zero degrees. Together with the improved motor control behavior, these results indicate that the optimization approach can handle the non-linearities of the muscle model. Spike activity show representative spike firing frequencies during the training process. The system operates within zero memory constraints and has an easily adjustable and well structured software architecture enabling scalability of the system. These findings support gra- dient descent optimization with forward propagation through time as a viable and scalable approach for embodied SNNs in motor control simulations, paving the way for more extensive applications like closed-loop cerebellum modeling.

This study investigates ”How does the modulation of the Soleus H-reflex vary across different levels of isometric voluntary contractions?” The hypothesis that the Soleus H-reflex amplitude follows a biphasic response, with an initial increase at lower contraction levels, followed by a plateau or decrease at higher levels of contractions. To test this, ten healthy subjects were recruited to perform isometric voluntary contractions at different levels of maximal voluntary contraction (MVC) (0%, 20%, 40%, 60%, and 80% MVC). The H-reflex peak-to-peak amplitude was measured and normalized as a percentage of the maximum M-wave (H/Mmax). The data were analyzed using a repeated measures ANOVA to assess the effect of MVC level on the Soleus H-reflex amplitude, followed by pairwise comparisons to identify significant differences between contraction levels. A repeated measures ANOVA revealed a significant effect of MVC level on Soleus H-reflex amplitude (H/Mmax) (F(4,32) = 4.82, p < 0.05). Pairwise comparisons showed a significant increase in H-reflex amplitude between 0% and 20% MVC (p = 0.013), while no significant differences were observed between higher contraction levels (20%, 40%, 60%, and 80% MVC). This pattern is indicative of the biphasic hypothesis, suggesting that the reflex amplitude significantly increases initially but plateaus as contraction intensity increases. These findings suggest that the initial increase and subsequent plateau in reflex amplitude may be influenced by changes in spinal excitability and inhibitory mechanisms as contraction intensity increases.

This paper investigates the effect of intrinsic muscle stiffness on neural control parameters in biological musculoskeletal control of stabilisation or reaching tasks. Current model implementations of intrinsic muscle properties are highly simplified, limiting their accuracy in replicating experimental short-range stiffness (SRS) behaviour, which appears to be important for stabilisation tasks. The Hill model, often used in musculoskeletal simulations, cannot account for SRS, while the Huxley model, which can account for non-linear muscle phenomena such as SRS , has a higher computational burden. The study compares a simplified Huxley-type model to two Hill-type models and determines the effect of intrinsic SRS on the control parameters of stabilizing 1- and 2-Degree of Freedom musculoskeletal models over various positive and negative stiffness positions in the force-length curve. Furthermore, the effect of the intrinsic muscle stiffness on the robustness of the feedback parameters of simple individual muscle feedback systems is determined in reaching experiments similar to classic experiments.

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.

Stroke patients can have spastic paresis of the lower leg, impeding an ankle which hinders gait. A novel orthosis has been developed which counteracts this impediment to the ankle. It is expected that gait training will improve stroke patients' use of the orthosis by increasing their ankle dynamics. Gait training with biofeedback, which is based on physiological signal, has been shown to be effective for stroke patients. The main design requirement for the biofeedback is that it facilitates learning of an increased active range of motion of the ankle. To fulfill this requirement the biofeedback is based on the maximum angle in plantar- and dorsiflexion during the swing phase of the impeded leg. In this research, the biofeedback is validated on healthy participants with an impeded right ankle performing five gait trials. First an unimpeded reference trial was conducted capturing normal gait. After which the participant's ankle was impeded. During the initial impeded trial the participant got accustomed to the impediment during gait. Then two trials with feedback were conducted followed by a retention trial without feedback. During the retention trial the effects the biofeedback has on the ankle dynamics are determined. The outcome measures were chosen to validate whether the biofeedback facilitated learning of an increased active range of motion of the ankle. The outcome measures were the increase of active range of motion of the ankle from the initial impeded trial to the retention trial and the error quotient. The error quotient is a measure showing to what extent the angles making up the active range of motion during a trial were the same as during the reference trial. The active range of motion of the ankle of participants increased (p < 0.001) from the initial impeded trial to the retention trial. Moreover a significant decrease in the error quotient of participants was found between the initial impeded trial and the first feedback trial (p = 0.033), second feedback trial (p = 0.013) and retention trial (p = 0.020). Therefore, the biofeedback facilitated learning of an increased active range of motion of the ankle to participants. Further research is required to determine how to best adapt the biofeedback such that it is suitable for use by stroke patients in daily life.

During human motor control, sensory information is integrated, and the influence of each cue depends on its variance. Sensory weighting within the proprioceptive system, specifically between force and position feedback, including its stiffness dependency, has been demonstrated for the shoulder and digits. Both studies encompass upper limb joints, but the impact of joint proximity on sensory integration remains unknown. In this study, we analyzed isolated vertical reaching movements of the wrist (palmar flexion), elbow (flexion), and shoulder (retroflexion) using a haptic robotic manipulator. We varied the stiffness constant of a virtual spring (15 N/rad, 50 N/rad, 80 N/rad) to assess the influence of environment stiffness on participants' ability to blindly reproduce a target force. A nonlinear spring was introduced to reveal the weighting strategy between force and position feedback. Differences in force and position when participants unknowingly operated in the nonlinear environment, compared to the linear environment, allowed us to calculate the weights of both feedback cues. Repeating the experiment for the three joints allowed us to assess the influence of joint location on the weighting strategy between force and position feedback. Ten participants performed a total of 720 reaches, covering three different stiffness conditions, three distinct joints, and ten separate runs, each consisting of eight reaches. We hypothesized that both environment stiffness and joint location have a significant impact on the weighting strategy. We expanded on existing findings that show a tendency toward up-weighting force feedback in the integration process as environment stiffness increases. Additionally, we examined whether force or position feedback is favored when the joint is more proximal. The hypothesis is supported by experimental evidence, as both stiffness and joint type revealed statistically significant effects on the weighting strategy (p $<$ 0.001, p = 0.020, respectively). As environment stiffness increased, a shift from higher position weighting factors to higher force weighting factors was observed for all three joints. When comparing our findings to the Maximum Likelihood Estimation model predictions, we observed that the more proximal joints exhibited higher force weighting factors. Additionally, normalizing the experimental environment stiffness conditions over the maximal voluntary force of each joint times the moment arm and over the joint stiffness showed a similar trend. More distal joints, on the other hand, favored higher position weighting factors.

Falls are a significant cause of injury-associated deaths. In cases where the events leading up to a fall are unclear, a forensic investigation may be required to uncover the cause. During the forensic reconstruction process, tools for objective scenario evaluation are needed. Computer simulations appear to be a promising tool for reconstructing falls, being cheaper in terms of both money and time than the alternative of physical scenario reconstruction. Although software packages intended for modelling the kinetics and kinematics of the human body exist, none were found that were validated specifically for fall reconstruction. The aim of the current study was to validate the performance of human body modelling software Madymo, intended for use in car-crash simulations, in reconstructing human falling movements. This was achieved by first performing experiments in which the kinematics and kinetics of participants were recorded during falls from a short height. Next, the initial conditions taken from the experimentally recorded falls were used as input to run corresponding simulations using Madymo. Finally, the results from the simulated falls were compared to those from the real falls, based on the posture just before landing. The results indicate that Madymo is currently not yet suited for use in reconstructing real human falls across multiple types of falls, and is therefore not yet fit for application in forensic investigations into falls.

Chronic inflammatory demyelinating polyneuropathy (CIDP) is characterized by sensory and motor deficits. Tremor is a frequent and severe symptom in patients suffering from CIDP. There is limited and contradictory data regarding the effect of tremor on treatment of the underlying polyneuropathy, and on treatment specifically aimed at neuropathic tremor. The pathophysiology of the CIDP tremor is unknown, but there are speculations about both a peripheral and central origin. From previous studies, it is seen that if a tremor’s frequency is substantially load-independent, entrained exclusively by comparable driving frequencies, and only minimally phase-reset by torque pulses, it is considered to be primarily of central origin. A wrist perturbation study using a wrist manipulator was conducted on CIDP patients, with tremors (n=8) and without tremors (control, n=8) to address these speculations, and to understand the characteristics of this neuropathic tremor. The perturbation study involved studying the effects of entrainment perturbations, spring loading via changing the virtual spring stiffness, ramp-and-hold stretches to evoke stretch reflexes, along with the estimation of admittance. The entrainment task showed subharmonic and superharmonic peaks of power spectral densities of EMG in
tremor subjects. Compared to controls, the tremor patients had significantly reduced stiffness, which could be attributed by the loss of muscle strength, and thus the tremor advertently affects muscle activation. No significant difference in M1, and M2 amplitude responses, and reflex latencies were found between the tremor and control group.

The compliance (stiffness) of a human arm varies, depending on the task at hand. Some precise tasks require high stiffness, while others need a level of flexibility to deal with unknown disturbances. To describe this human behaviour a portable device is needed to give small force perturbations (input) while the resulting reaction of the arm (output) is measured. There are multiple state-of-the-art devices to choose from to provide such input. However, the majority of these devices either offer too little versatility or impede the free movement of the user. Implementation of a novel parallel mechanism allows for a lightweight (0.175kg) and compact device that can generate various signals in three degrees-of-freedom. Since the device is designed to be mounted around the wrist, it leaves your arm and hand unobstructed. A full-scale prototype is constructed and the concept is tested using a force sensor. Implementing powerful yet compact servo motors allows for controlled perturbations in the order of 4N, with bandwidths up to 12Hz.

The Delft Hand and Wrist model is a recently created musculoskeletal model in the OpenSim environment. The current model lacks a validation of the thumb muscles. Therefore, the main goal of this work is to perform a quantitative trend validation by analyzing the correlation between experimentally measured muscle activity data and muscle forces estimated by the model from markerless motion capture data. The original model is reduced to a minimal model containing only the thumb muscles. A second model is created by changing optimal muscle fiber length, maximum isometric force and tendon slack length parameter values in the minimal model to values reported in literature to evaluate the effects of these adjustments on the estimated muscle force. An experiment is conducted in which participants are instructed to perform repetitive thumb motions while muscle activity and 2D kinematic data is captured. 3D kinematics are obtained through the machine learning toolboxes DeepLabCut and Anipose. Muscle forces are estimated through inverse dynamic static optimization in OpenSim. The results showed that the correlation between the estimated muscle forces and experimentally measured muscle activity data for both the minimal model and the adjusted model was very low to moderate, meaning both models yield unrealistic muscle force estimations. In contrast, the measured muscle activity and kinematic data show expected results thus are captured correctly, which is useful for reference in future work.
There is room for improvement of the Delft Hand and Wrist model. Nevertheless, this work provides suggestions for the optimization of the current model and paves the way towards muscle force estimation and quantitative validation using experimentally measured muscle activity data for the Delft Hand and Wrist model in OpenSim.

Of all reported cases, muscular pain in the arms is the second main cause of work-related musculoskeletal disorders (WMSDs) in European workers. The most popular prevention method for shoulder-related WMSDs are passive shoulder exoskeletons, as of their light weight and ease of use. Yet, due to their static force-angle characteristic, passive shoulder exoskeletons show a limited load reduction effect for dynamic tasks. Active shoulder exoskeletons show better performance in the reduction of loads for dynamic tasks, but are not accepted in industrial environments due to their high weight and complexity. In this study, the advantages of both passive and active shoulder exoskeletons have been combined in the design of a mechanism that can actively alter the force-angle characteristic of the Skelex 360-XFR passive shoulder exoskeleton, increasing its load reduction performance, and hence making it a more effective prevention method to WMSDs. By experiments using a prototype, it has been shown that a drivable parallelogram linkage capable of inducing angular changes between an in-going lever arm link and an out-going link to the user’s arm, can effectively induce proportional phase changes of the conventional force-angle characteristic, and that the model predicting the optimal stiffness of the spring used in the implemented force-balancing mechanism is reliable. With the experimental findings, recommendations have been made regarding the minimum capacity in output torque of the actuator, 0.38 Nm, and the stiffness of the balancing spring, 1752 N/m, to end up with optimal weight, size, and power consumption of the semi-passive shoulder exoskeleton.

Parkinson’s Disease (PD), Essential tremor (ET), and dystonia are movement disorders often misdiagnosed as one another and commonly present tremor as one of their motor symptoms. Rates of misdiagnosis between 30 and 50% of ET patients have been reported, where dystonia and PD are the most common missed diagnoses. Additionally, up to 50% of dystonia cases are misdiagnosed/under-diagnosed at their first encounter. Misdiagnosis rates up to 34% are reported for PD. Although similar tremor behaviors between the mentioned disorders lead to substantial misdiagnosis rates and, consequently, subpar care, tremorous signal acquired via wearable sensors can be used to discriminate between PD, ET, and dystonia patients. This study aims to develop three proofs-of-concept, accelerometer-based algorithms to assist medical doctors with decision-making in unclear diagnostic scenarios.

Tele-impedance can increase interaction perfor- mance between a robotic tool and unstructured/unpredictable environment during teleoperation. However, the existing tele- impedance interfaces have several ongoing issues, such as long calibration times and various obstructions for the human oper- ator. In addition, they are all designed to be controlled by the operator’s arms, which can cause difficulties when both arms are used, as in bi-manual teleoperation. To resolve these issues, we designed novel foot-based tele-impedance control method inspired by the human limb stiffness ellipse modulation. The proposed mechanical interface is based around a circular disc and a foot pressure sensor that controls orientation and size/shape of the stiffness ellipsoid, respectively, by the operator’s foot. We evalu- ated the circular disc interface control method in an experimental study with 12 participants, who performed a two-dimensional drilling task in a virtual environment. The results show the ability of the operator in controlling the proposed interface to dynamically adapt to task instruction and environment. In addition, a comparison with low and high constant impedance control demonstrate a superior interaction performance of the proposed interface.

The automotive industry is foreseeing a future where driver and car will form a synergy, by allowing the car to predict the driver’s intent. Nissan has already successfully predicted the intent to make lane changes in a driving simulator with just six electroencephalography (EEG) electrodes on the movement related cortical potential using Machine Learning. This thesis assesses if the prediction can be improved with EEG layouts containing more electrodes. A large data set was recorded with a similar lane change paradigm in a driving simulator as Nissan’s original study. Seven predefined EEG layouts were assessed: including 4, 9, 18, 25, 32, 48 and 64 electrodes. The linear discriminant analysis classifier was used offline to classify two classes: driving straight or steering. Secondly, to assess if a different selection of electrodes could have performed better, partial least squares (PLS) regression was used to select electrodes based on importance. Lastly, it was also assessed if the classifier could predict the direction of lane change as well as intention, in other words, if the data set consisted of three classes: steering left, steering right and driving straight. There was a statistical difference between the performance of the seven layouts with two classes, but from Layout 3 (with 18 electrodes) and up this difference was no longer significant. The selection of electrodes based on PLS did not prove to be better than the predefined layouts. When the data is divided into three classes, for the prediction of direction, similar results were seen: there was a statistical difference between the layouts and Layout 3 proved to be sufficient. Performance improves with more electrodes, but this effect plateaus. Layout 3 contained a satisfactory amount of electrodes, namely 18, to predict lane change intent. Moreover, the direction of the intended lane change can also be predicted sufficiently with this layout.