FineRehab: A Multi-modality and Multi-task Dataset for Rehabilitation Analysis

Jianwei Li, Jun Xue, Rui Cao, Xiaoxia Du, Siyu Mo, Kehao Ran, Zeyan Zhang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 3184-3193

The assessment of rehabilitation exercises for neurological and musculoskeletal disorders are crucial for recovery. Traditionally assessment methods have been subjective with inherent uncertainty and limitations. This paper introduces a novel multi-modality dataset named FineRehab\footnotemark[4] to prompt the study of rehabilitation movement analysis leveraging advancements in sensor technology and artificial intelligence. FineRehab collects 16 actions from 50 participants including both patients with musculoskeletal disorders and healthy individuals and consists of 4215 action samples captured by two Kinect cameras and 17 IMUs. To benchmark FineRehab we present a reliable approach to analyze rehabilitation exercises and make experiments to evaluate the comprehensive movement quality from across multi-dimensions. Comparative experimental analyses have verified the validity of our dataset in distinguishing between the movement of the normal population and patients which can offer a quantifiable basis for personalized rehabilitation feedback. The introduction of FineRehab will encourage researchers to apply develop and adapt various methods for rehabilitation exercise analysis.