Yiran Jiao

Instrumented gait analysis (IGA) has been widely used in research, but not typically in clinical practice, as it requires expertise in data analysis and interpretation. Post-stroke clinical gait assessment could be improved by integrating artificial intelligence into IGA, but previous gait assessment systems have relatively low clinical utility. This study aims to develop a clinically oriented automatic post-stroke gait assessment system based on knowledge graph (KG) to better support clinicians. A domain KG is first constructed in the field of gait analysis. This system can process IGA data to identify gait abnormalities and their potential causes based on kinematic analysis and KG. A preliminary evaluation with twenty post-stroke patients and four domain experts tested the system's performance in clinical settings, showing an average recall, precision, and F-score of 1, 0.78, and 0.89. Four clinical professionals showed high behavioural intention to use the system in clinical settings (4.33 ± 0.41 on a 5-point Likert scale based on the Technology Acceptance Model). The results depicted that this system has potential to be applied in clinical settings to provide useful supplementary insights for clinicians, which may promote the interpretation and clinical utility of IGA. The schema of this KG could be generalised or extended to gait analysis related to other diseases.

Clinical use of Virtual Reality (VR)-based treadmill training to improve post-stroke gait is limited by a lack of adherence to motor learning and control principles, and user-centred design consideration. This study aims to outline the development and evaluation of a VR-based treadmill training system for post-stroke patients, aligning with currently accepted rehabilitation concepts and principles. The proposed system involves several modules and algorithms (e.g., biomechanical model, feedback controller, adaptive difficulty controller) along with portable motion capture technology to provide individualised rehabilitation focused training. Eight physiotherapists and eleven post-stroke participants were recruited to evaluate the system’s acceptability, usability, and adherence to motor learning and control principles. The physiotherapists reported high ratings in terms of the design’s adherence to motor learning and control principles, and both physiotherapists and post-stroke patients reported positive intention to use the system. The proposed system promotes individualised and progressive practice and feedback for post-stroke gait training in VR environments. Balancing technical complexity, practical considerations, and adherence to motor learning and control principles in this system can enhance the application of VR interventions for post-stroke patients in both clinical and remote rehabilitation settings.