Validation of a kinect-based system to quantify proximal arm non-use after a stroke

Introduction/Background Proximal arm non-use is when an individual with a weaker arm can use it, however spontaneously persists in under-using shoulder and elbow joints. The score of proximal arm non-use (PANU) is computed as the difference between the spontaneous use and the maximal use of shoulder and elbow joints. We previously quantified PANU using an ultrasound and marker-based movement analysis system (Zebris, CMS20s ® ). The aim of this study is to validate a low-cost markerless system (Microsoft Kinect ® -based) against the reference system (CMS20s ® ) to determine PANU during seated reaching. Material and method In 19 hemiparetic stroke individuals the PANU score, reach length, trunk length, proximal arm use were measured during seated reaching simultaneously by the Kinect ® (v2) and CMS20s ® over two testing sessions separated by 2 h. Results Intraclass correlation coefficients (ICC) and linear regression analysis showed that the PANU score (ICC = 0.95, r 2  = 0.90), reach length (ICC = 0.82, r 2  = 0,70), trunk length (ICC = 0.97, r 2  = 0.94) and PAU (ICC = 0.97, r 2  = 0.93) measured using the Kinect were strongly related to those using the CMS20s. The PANU scores showed good test-retest reliability for both the Kinect (ICC = 0.76) and CMS20s (ICC = 0.72) with Bland u0026 Altman plots showing slightly reduced PANU scores (i.e., improved performance) in the re-test session for both systems (Kinect: −4,25 ± 6.76; CMS20s: −4,71 ± 7.88), which suggests a practice effect. Conclusion We conclude that the Kinect ® can offer a low-cost and widely available solution to clinically assess PANU to monitor the progress of paretic arm recovery and to better individualise rehabilitation.