Stroke survivors often struggle with gait asymmetry post-therapy. Researchers are exploring Virtual Reality (VR) to address this problem with the help of visual feedback and virtual avatars. VR also makes repetitive tasks more enjoyable, improving patient compliance and outcomes. Motor adaptation, essential in rehabilitation, involves adjusting movements to new conditions. Studies have used forms of motor adaptation with visual feedback to distort participants' gait symmetry, making people walk asymmetrically. This approach is called implicit Visual Feedback Distortion (VFD), where visual feedback is manipulated without the user's awareness. This thesis explores using implicit VFD with avatars in immersive VR to address gait asymmetry. An experiment with 11 healthy participants tested implicit VFD by gradually increasing the step length of the avatar's right leg. Contrary to previous screen-based VFD studies, results showed no significant effect on gait symmetry. Additionally, no correlations were found between step symmetry and psychological states (presence, embodiment, motivation). We hypothesize that the high distortion level on the right foot, suboptimal virtual environment design, and reported neck pain contributed to these findings. Future research should explore different VFD designs, or look at multiple groups to see what conditions lead to more gait asymmetry during adaptation.

Rehabilitation following a stroke plays a crucial role in functional recovery and the regaining of independence. Advanced rehabilitation robots such as the Lokomat, C-mill, Rysen, and ZeroG have become integral tools for gait rehabilitation. The MATE (Minimally Actuated Tendon-based Exercise Environment) is a novel rehabilitation device that could offer a wider range of motion compared to the Lokomat while providing support for leg movement, unlike the C-Mill, Rysen, and ZeroG. However, initial testing on the MATE indicated that this passive tendon-based rehabilitation device lacks a proper connection to the wearer. Consequently, this study was conducted to design and evaluate an attachment model, ensuring that the pulling forces are comfortably transferred to the body and that the MATE fulfills its purpose of supporting and enhancing the natural gait. The study compared two user-centred attachment designs based on user experience, comfort, usability, acceptance, ability to follow the natural gait, and the difference in pulling forces on the legs. The findings revealed that the bandage design exerted greater control over the legs, while the cuff design provided more guidance. Additionally, it was observed that adhering to the user’s natural gait greatly influenced comfort and that walking with the MATE became easier with practice. Although the bandage design was preferred, the results suggest further iterations to enhance gait support, comfort, and usability.

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.

Falling remains a large source of (traumatic) injuries and healthcare costs. Over the past years, different actuators have been developed in the field of wearable robotics to help prevent injuries from falling. To increase the wearability of these systems, the weight of power storage can be decreased with intermittent instead of continuous control. A fall detector is then needed for these systems to trigger the activation of the actuator to prevent the fall. A proof of concept for a model-based fall detector that is aimed at using only wearable sensor measurements is presented. The algorithm is based on a single inertial measurement unit placed on the lower back. The upper-body orientation and centre of mass velocity are estimated with two separate Kalman filters. The velocity is estimated with a gait model consisting of a spring-damper-legged point mass in three-dimensional space. The balance of the subject is evaluated with the velocity estimates and the extrapolated centre of mass method. The presented model is verified on a non-falling treadmill walking dataset of real humans and shows accurate estimates of the centre of mass velocity. Furthermore, a planar falling simulation is performed to show successful pre-impact fall detection. This resulted in no false alarms outside the initial estimation settling time, and a successful detection with a lead time of 680 ms. This lead time is long enough to provide a trigger for fall mitigation devices.

According to the WHO, falls are responsible for over 38 million disability-adjusted life years lost each year, globally. Additionally, an estimated 684.000 individuals die of falling each year, making it the second leading cause of unintentional death. One of the most effective physical activity interventions to reduce the risk of falling, is targeted exercise that safely challenges balance. One tool, that is currently
being developed, which can aid physical therapists with these interventions is the GyBAR. A wearable device that uses gyroscopes to apply moments to the patient. This can be used to either provide balance assistance or challenge balance by applying perturbations. The goal of this research was to develop an interface for the GyBAR. The interface should contribute to the acceptance of the GyBAR. This can be achieved by an excellent perceived ease of use, which combined with perceived usefulness
are indicators for the Technology Acceptance Model (TAM). The interface inspired by a Voodoo-doll was developed by going through the following design process steps; the creation of a list of requirements; a brainstorm to generate ideas; the development of three concepts; and the selection and development of one concept into a prototype. The Voodoo Doll controls the GyBAR by a handheld model of the patient, which can be manipulated and translates the movements of the model to the actual patient. User tests were performed to validate the design. With a SUS-score of 82.81 (SD=7.48), which is within the 90-95 percentile, it can be concluded that the interface has an excellent perceived ease of use. Further development is encouraged are several recommendations for points of improvement are given.