Evaluating Clinical Efficacy of Optical Motion Tracking with Real-Time Animation for Rehabilitation Monitoring

Abstract: Rehabilitation is a cornerstone for recovering motor function following conditions such as stroke, Parkinson’s disease, aging, or surgery. These conditions often result in musculoskeletal, neurological, or sensory dysfunctions that impair Activities of Daily Living (ADL), delaying recovery. Clinical studies have shown that repeated rehabilitation programs significantly enhance recovery outcomes. Motion capture systems (mocap) provide a robust method to monitor rehabilitation progress, diagnose biomechanical disorders, and adjust treatment plans. This study evaluates the clinical effectiveness of optical motion tracking systems by conducting quantitative kinematic analyses and integrating real-time animation streaming. The study utilized the OptiTrack motion capture system equipped with seven Flex 13 optical cameras and Motive Tracker software to record movement data. Five healthy participants (aged 19–29 years, M=23, SD=3.67; height 170–195 cm, M=181.6, SD=9.63) were recruited to perform three biomechanical tasks: gait, single-leg squat jump, and straight-leg sidewalk. Movements were analysed under normal and braced conditions to investigate key kinematic variables: knee flexion, dorsiflexion/plantar flexion, and hip abduction. Motion capture data was processed in MATLAB for 3D transformation matrix calculations, and the results were streamed into Unity3D to create real-time animations for visualization.Statistical parametric mapping (SPM) paired t-tests (α=0.05) revealed significant differences between normal and braced conditions. During gait, knee flexion range of motion (ROM) was reduced by 32.06° under braced conditions, indicating a limping motion with limited vertical swing-phase movement. In the single-leg jump test, braced conditions resulted in a 7.2° increase in plantar flexion and reduced jump height, while normal conditions demonstrated greater dorsiflexion and knee flexion, indicating compensation. For the sidewalk test, braced conditions showed reduced hip abduction and increased lumbar engagement, with lumbar flexion increasing from 16.82° to 30.89°, suggesting alternate muscle activation patterns. Real-time animation in Unity3D successfully visualized participant biomechanics, offering potential as a recreational and engaging tool for rehabilitation. While the system provided statistically significant data and detailed motion analyses, there are some limitations in the current approach. The rigid-body tracking method required extensive filtering to mitigate inaccuracies caused by marker occlusion and auto-solve algorithms. This approach differs from dynamic skeletal models, which typically integrate lower-body kinetic plugins, and therefore required additional post-processing to accurately calculate joint angles. In real-world applications, factors such as environmental conditions (e.g., lighting and reflective materials), patient behaviour, and movement variability must be considered. Expanding the study to include a diverse range of injury types and larger, more varied sample sizes will be important for understanding how motion capture can be seamlessly integrated into rehabilitation programs. Machine learning could automate the processing of motion data, enabling faster diagnosis and real-time tracking of movement disorders, such as gait abnormalities or Parkinson's symptoms. Furthermore, the integration of virtual reality (VR) with real-time animation could create more immersive rehabilitation experiences, enhancing patient engagement and motivation during therapy. Real-time applications could also enable home-based monitoring, allowing patients to continue their rehabilitation remotely, while clinicians track progress and provide personalized feedback from a distance. Augmented reality (AR) apps could further enrich this experience, guiding patients through exercises with interactive visuals and real-time feedback.This study highlights the growing potential of motion capture systems, combined with advanced computational tools, to transform rehabilitation. By optimizing motion tracking, enabling more personalized treatment, and improving patient engagement, these technologies have the capacity to enhance recovery outcomes and improve the accessibility of rehabilitation programs.

Keywords: Motion capture, Rehabilitation monitoring, Optical tracking, Biomechanics, Real-time animation

DOI: 10.54941/ahfe1006200

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