This study investigated handrail usage patterns in stroke hemiplegic patients during stair descent. A handrail sensor was used to measure the force direction on the handrail of 16 patients, and the acquired data was analyzed. Using the clustering algorithm, the force direction data was classified into two patterns: pulling (positive shear force) and pushing (negative shear force). A significant difference was observed in the paretic hip abduction angle, with increased abduction in the pulling pattern and decreased abduction in the pushing pattern. These differences suggest distinct movement strategies to compensate for pelvic tilt corresponding to handrail usage angle.

The integration of adaptive learning and AI gamification has opened new possibilities for personalized education, particularly for students with severe learning difficulties (SLD) and profound and multiple learning disabilities (PMLD). Despite these advancements, there is a significant lack of tools capable of dynamically measuring and adaptively challenging students' physical response times, which are critical for motor skill development and engagement. This study investigates the impact of rule based adaptive difficulty in a "Whac-a-Mole" game on physical response metrics, specifically reaction time and hit consistency. By utilizing AI algorithms, the system dynamically adjusts task difficulty based on individual performance, ensuring an optimal balance of challenge and ability. Additionally, the research aims to establish baseline physical response data for each student with SLD and PMLD to better identify milestones, track progress, and support individualized learning goals. Preliminary findings suggest that the adaptive approach enhances engagement, improves response time consistency, and provides valuable data for educators and therapists to monitor physical and cognitive development. This study contributes to the development of dynamic, data-driven tools that foster measurable progress in physical response skills within adaptive educational settings.

Turning is a high-risk activity for falls. The Timed Up and Go Test (TUG), commonly used to assess fall risk, involves standing, walking, and turning. However, the cone used in the TUG does not replicate the spatial separation seen in daily life, such as walls and furniture. The effect of different turning center designs on gait is not apparent, thus potentially overlooking gait characteristics due to daily environments. This study investigated the effect of turning center design in TUG using a pole to obstruct the upper space and cone as conventional. Sarcoma patients were selected because their motor function is often impaired due to prosthetic or artificial joint surgeries, making them suitable for comparison with healthy individuals. Participants performed TUG using a cone and pole, respectively, and their joint angles and spatiotemporal parameters were measured. Results showed significant group differences only with the cone, where patients exhibited smaller trunk lateral flexion and lumbar elevation angles than healthy individuals. However, with the pole, the trunk lateral bending angle during turning widely varied in patients. These findings suggest that while the cone helps assess disease-related functional changes, poles may be more appropriate for evaluating gait abnormalities depending on disease severity.

This study aimed to develop a mixed reality (MR) application for the rehabilitation of individuals with hemineglect. The proposed application involves an activity to practice visual scanning and to stimulate the lower limbs through interaction with virtual objects alternately generated on both sides of the individual.

MR devices do not natively support interaction via lower limbs. Therefore, different methods for integrating this functionality were analyzed. After exploring various possibilities, the most viable options were the MR device's controllers and the integration of a 3rd party external tracking system, both attached to the feet. Consequently, an in-depth comparison was conducted between the Meta Quest 3 controllers and the HTC VIVE Ultimate Trackers.

In addition, for more accurate exercise evaluation and monitoring, a specific module was programmed to acquire limb angle data using a digital goniometer via Bluetooth. This data source, combined with live visualization of the activity, provides healthcare professionals with a valuable tool for assessing the individual’s progress in the rehabilitation process.

The Mini-Balance Evaluation Systems Test (Mini-BESTest) is a widely utilized clinical tool designed to assess dynamic balance and functional mobility in individuals with balance impairments. It evaluates four key domains of balance: anticipatory postural adjustments, reactive postural control, sensory orientation, and dynamic gait. Despite its clinical utility, scoring the Mini-BESTest relies heavily on subjective human assessment, which introduces variability in settings requiring frequent and longitudinal monitoring. In recent years, assistive technology based on inertial measurement units (IMUs) have emerged as a promising technology to provide quantitative, continuous and more objective measurements of human movement. By leveraging sensors embedded in IMUs, it is possible to capture detailed kinematic information relevant to balance and mobility. We investigated the feasibility of scoring Mini-BESTest exercises using assistive technology based on 3-axis accelerometer sensors embedded in IMUs positioned at four distinct body locations. For each Mini-BESTest exercise, we analyzed at which extent IMUs can be used for scoring, highlighting strengths and weaknesses and possible overcomes.