Due to neuromuscular capacity decline, sit-to-stand, an essential daily life activity, is increasingly difficult to perform for elderly. In practice, elderly frequently use compensatory arm-strategies in sit-to-stand. However, the specific advantages of these compensatory strategies are unclear. The focus of this research is to study the influence of thigh push-off, a common compensatory strategy, on the stability of sit-to-stand for both the young and elderly. Motion data was retrieved from 50 young and elderly participants who performed sit-to-stand with and without thigh push-off. To mimic realistic daily-life strategies, participants were not restricted in sit-to-stand style in any way but arm-use. The experimental sit-to-stand data was fitted to a pendulum model with feedforward and feedback control to simulate the stability limits of the observed sit-to-stand strategies. The model allows us to explore stability limits without the need of perturbation. For each arm-strategy and participant a stability basin, i.e. all potential trajectories of motion, was formed. We compared the stability basins between arm-strategies and between age-groups. The size of the computed stability basin for thigh push-off was larger than for no arm push-off implicating thigh push-off is a more stable sit-to-stand strategy. The difference between arm strategies was larger for the elderly than for the young participants. Overall, the stability basins of the elderly were larger compared to those of the young participants, for both arm strategies. The resulting stability basins suggest that thigh push-off increases the stability of sit-to-stand and thus could indeed be an effective compensatory strategy. Stability basin shape differences indicate that without thigh push-off elderly compensate for possible early sit-down and a step halfway sit-to-stand, and with the thigh push-off strategy only for early sit-down. The age-groups differences confirm stability basins quantify the stability of the observed movement strategy rather than the stability of the participants. We observed that elderly use more precautious sit-tot-stand movements.

As the population aged 65 and above increases, falls among these older adults emerge as a significant public health concern, leading to disabil- ities and economic burdens. Preventative strategies and personalized fall risk assessments are essential for mitigating fall risks. Human Activity Recognition in early fall risk detection by monitoring everyday ac- tivities in older adults could assess patients fall risks. However, current literature has overlooked the older adult demographic by only measuring adults younger than 65, under representing the older population. This research specifically focuses on identifying key features from head-mounted Inertial Measurement Unit (IMU) data using machine learning to classify Sit-to-Walk (STW) and Walk-to-Sit (WTS) move- ments, which are commonly associated with high risk of fall. In addition these movements can be essential in monitoring changes in performance to asses fall risk. We analyzed five activities STW, WTS, Sitting, Swing phase, and Others. Using three feature selec- tion methods (Mutual Information Gain, ANOVA, Recursive Feature Elimination) on 116 extracted fea- tures we were able to rank the features and select the top ten. The study then evaluated the accuracy of three classifiers (Logistic Regression, Random For- est, and K-Nearest Neighbor or Support Vector Ma- chine) with these features. Results indicated that the ANOVA and Random Forest classifier combination achieved the highest total accuracy of 95%, with Ran- dom Forest performing exceptionally well in STW and WTS classifications, reaching up to 81% accu- racy. Commonly selected features across all methods included the accelerometer’s maximum x-axis mea- sured and its energy in both time and frequency do- mains. This model’s performance is comparable with existing literature and validates its effectiveness in fall risk detection.

Skateboarding involves a human controlling a four wheeled vehicle that is steered by tilting the standing surface. The riding mechanics of skateboarding have been well reported [2, 3]. The sport also includes aerial maneuvers such as jumping of stairs, flying off ramps and flipping and rotating the skateboard. The most basic aerial trick is called the ollie. The athlete jumps up while pushing down on the back end of the skateboard’s tail, causing a rotation about the back axle. The upward acceleration due to the rotation together with the tail-ground impact cause the skateboard to go airborne. Midair the athlete drags the skateboard up through frictional contact and levels it out to land the trick. The most concrete performance measure of the ollie is height according to the Olympic judging criteria [4]. To reach maximum height the dynamics such as impact, dynamic response, and torque production are dependent on shape, inertia and mass, which gives reason to assume an optimal shape exists. This leads to the research question: What are the optimal geometric and inertial parameters of a skateboard for an Olympic athlete to reach maximal ollie height. The skateboard geometry is optimized through multiphase direct collocation with the objective of maximal ollie height. A parameterized model is created with scaling mass and inertia properties such that the geometry of the skateboard. Modelling the dynamics of the ollie including impact and friction are done with a point mass human controller that is kineticly and kinematicly mapped to a counter movement jump. A simplistic contact implicit impact scheme is made for a higher order optimization. The ollie height is improved by changing the mass and inertia properties of the skateboard. Multiple optimal board shapes are generated for example a skateboard with a smaller wheelbase can reach higher ollie height compared to an industry standard skateboard.

At Basalt rehabilitation clinic The Hague, gait analysis is currently mostly observational. No objective measures are used and there is no systematic use of recordings. The aim of this research is to find and verify an accessible instrumented gait analysis method that could be implemented in the current care path at Basalt The Hague. The current instrumented gait analysis systems are marker-based. Recently more research has been done with regard to markerless systems, since markers bring along artefacts like the soft-tissue artefact. Markerless systems work with pose detection through artificial intelligence. A limitation of these pose detection systems, is that it is still in a research phase and did not make its way into the clinics yet. In a comparison of the accuracy of open-source pose estimation methods for measuring gait kinematics, OpenPose has been found most accurate. For this research a program of requirements is made for implementation of instrumented gait analysis in the clinic at Basalt The Hague. The coordinates of anatomical landmarks obtained with OpenPose are processed to kinematic parameters. Besides, spatiotemporal parameters are looked into. A gait analysis set-up (consisting of tripods and mobile phone cameras) with OpenPose is proposed and tested to verify the accuracy. The OpenPose outcomes are compared with the the Simi motion capture system at Basalt Delft. By determining the Pearson correlation coefficients between OpenPose and Simi motion capture system, the kinematic parameters are verified. The mean standard deviations of the repeated recordings with OpenPose are used to determine the repeatability of OpenPose. With this research is verified that OpenPose is repeatable and that kinematic parameters of the hip and knee are highly correlated to the standard method of instrumented gait analysis used at Basalt. Therefore can be concluded that OpenPose pose detection seems a promising method for determining kinematic and possibly also spatiotemporal parameters of gait. With follow-up research, especially clinical validation, and further development of the data processing, the first steps can be made towards implementation of accessible instrumented gait analysis in the current care path at Basalt The Hague, and possibly other locations and/or institutions.

Robots can be powerful tools in post-stroke gait rehabilitation. However, state-of-the-art robots are often expensive machines containing rigid links with high inertia. Their expensiveness could limit their availability, and their high inertia reduces transparency, which could hinder rehabilitation progress. These factors raise the need for a minimalistic transparent robot that can effectively fill this gap. This research aims to design and validate such a device, a minimally actuated tendon-based exercise environment. The device is synthesized using an optimization algorithm that considers possible system configurations and optimizes both these configurations and their respective design parameters. Validation is done based on a reconstructed simulation of gait using motion capture on the optimal design to check whether it could be used for real-life rehabilitation. It was found that the most simplistic solution is not yet adequate for rehabilitation; thus, a slightly more complex design is required. While not providing the final solution, this research provides an important stepping stone towards designing a minimally actuated, simplistic, and transparent rehabilitation device.

Biomechanics studies the underlying mechanisms between body movements and forces. Accurate motion data is crucial for the biomechanics. Currently, marker-based motion capture systems are often used by researchers to record motion data. Marker-based motion capture systems are not widely adopted due to its drawbacks in terms of financial and time costs, portability, etc. Video-based motion capture systems can record motions using videos collected by webcams, cameras, and smartphones as the input and then estimate human motions from those videos. A simpler setup makes video-based motion capture technology more accessible for widespread use. However, existing motion capture datasets commonly lack of biomechanically accurate annotation, resulting in a deficiency in the biomechanical accuracy of exsisting video-based motion capture methods. In the biomechanics community, there are a lot of validated and biomechanically accurate models and motion data; however, corresponding video data is lacking. We can construct human-like appearance based on these data and generate a synthetic human motion video dataset using 3D graphic software. In this thesis, we purposed a pipeline that can generate synthetic human motion videos. The pipeline takes subject-specific OpenSim model and motion as input and uses SMPL-X model to generate human-like appearance. We validated the synthetic data generated by our pipeline and demonstrated the biomechanical reliability of the pipeline. Using this pipeline, we created synthetic dataset ODAH with biomechanically accurate annotations for neural network training.

Muscle fatigue's indirect link to higher athletic injury risks is a key focus of this study. It highlights how fatigue-induced shifts in muscle resource allocation and movement patterns can lead to biomechanical imbalances, subsequently heightening injury susceptibility. Addressing high injury rates in athletics, this study was conducted in two pivotal phases: the development of a wearable, textile-integrated surface electromyography (sEMG) garment, and the identification of the most effective real-time fatigue metric for true wireless detection for dynamic exercise. While traditional sEMG methods provide valuable insights in laboratory settings, they fall short in dynamically and individually monitoring muscle fatigue in real-world scenarios. 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 Dutch speedskating federation expressed the need for a feedback system for elite long-track speedskaters that can aid them in finding the optimal technique for an individual athlete. To contribute to this goal, this research aims to develop an optimization workflow that can reproduce realistic steady state speedskating behaviour. This is done with use of the simple skater model (SSM) (Van Der Kruk, Veeger, van der Helm, & Schwab, 2017). The research consists of two phases. The first to verify if the optimization can produce realistic speedskating motion, the second optimizes the speedskating technique to minimize the duration of one stroke. The optimization is solved with IPOPT. Even though the first phase can reproduce realistic speedskating motion, the optimal technique found in the second phase was unrealistic in terms of both trajectory and applied forces. This is caused by an inconsistency in the heading of the skate. This research shows the capabilities and limitations of optimizing the speedskating technique with the SSM.

Introduction: A thigh push-off is often used as a compensation strategy for standing up by elderly people. However, the biomechanics of this movement are not known. In this thesis the standing-up movement with the use of the thigh push-off strategy (TP), the armrest push-off strategy (AR) and the no arm aid strategy (NA) was analysed. The aim was to find out why TP for standing up is being used as a compensation strategy by elderly people. Method: We examined upper and lower limb joint moments and lower limb muscle forces in three different sit-to-stand strategies in nine healthy elderly men. Inverse dynamics and static optimisation were done in OpenSim using a 3D musculoskeletal model. Results: The lumbar extension moment in TP was significantly lower compared to NA (p=0.04). Rectus femoris force is lower in phase 2 in TP compared to AR. AR upper limb joint moments were significantly larger in dominant and non-dominant shoulder external rotation (p=0.02, p<0.01), elbow extension (p<0.001, p<0.001), and wrist flexion (p=0.04, p=0.02) compared to TP. Also, dominant shoulder abduction (p=0.04) moment was higher in AR compared to TP. Conclusion: Elderly people probably use a thigh push-off to unload the lower back, but this could also be accomplished with AR. However, AR upper limb loading is higher com- pared to TP. TP is used to unload the lower back and upper limb joints.

Lower-limb exoskeletons often use trajectory-tracking control to define the device's motion and assistance level. One challenge lies in ensuring a smooth and comfortable interaction between the user and the robotic device by defining a reference trajectory. While recent research has focused on generating individualized gait patterns based on user-specific body characteristics and walking speed, limited research has explored the subjective perception of these patterns and their impact on user experience and rehabilitation outcomes. This study investigates user perceptions of individualized versus standard and random gait patterns, focusing on enjoyment, comfort, and naturalness. A predictive gait pattern model, incorporating individual data and walking speed, was developed and tested with human participants using a grounded robotic lower limb device. Participants compared the three gait pattern types and provided subjective feedback through a questionnaire. Findings indicate no significant preference for any gait pattern in terms of enjoyment, comfort, and naturalness, except for physical strain where the predicted pattern caused significantly more strain than the standard. The analysis also revealed that longer engagement with the device led to increased comfort and naturalness, suggesting an adaptation effect. A general tendency towards preferring the standard pattern was noted, though further research is necessary to determine whether a larger sample size reveals significant differences. Additionally, the perception of different gait patterns and their effect on the rehabilitation outcome should be explored with stroke patients.