Introduction: Interactive game systems can motivate stroke survivors to engage with their rehabilitation exercises. However, it is crucial that systems are in place to detect if exercises are performed correctly as stroke survivors often perform compensatory movements which can be detrimental to recovery. Very few game systems integrate motion tracking algorithms to monitor performance and detect such movements. This paper describes the development of algorithms which monitor for compensatory movements during upper limb reaching movements in real-time and provides quantitative metrics for health professionals to monitor performance and progress over time. Methods: A real-time algorithm was developed to analyse reaching motions in real-time through a low-cost depth camera. The algorithm segments cyclical reaching motions into component parts, including compensatory movement, and provides a graphical representation of task performance. Healthy participants (n = 10) performed reaching motions facing the camera. The real-time accuracy of the algorithm was assessed by comparing offline analysis to real-time collection of data. Results: The algorithm's ability to segment cyclical reaching motions and detect the component parts in real-time was assessed. Results show that movement types can be detected in real time with accuracy, showing a maximum error of 1.71%. Conclusions: Using the methods outlined, the real-time detection and quantification of compensatory movements is feasible for integration within home-based, repetitive task practice game systems for people with stroke.
Keywords: Assistive Technology; Motion Analysis Systems; Motion/ Posture Analysis; Stroke Rehabilitation; Virtual Reality.
© The Author(s) 2022.