Research on Rehabilitation Training Movement Recognition and Real-time Feedback Model Based on Computer Vision

Abstract

Aiming at the key problems such as the separation of action recognition and quality assessment tasks, coarse feedback granularity and high labeling cost in the automatic evaluation of rehabilitation training, this paper proposes PDDS‑Net. The framework takes human skeleton sequence as input and realizes action classification and location-level deviation location synchronously through the collaborative architecture of a global action recognition stream and a local part evaluation stream. In the local flow, the joint nodes are divided into three functional part groups: upper limb, trunk and lower limb. independent graph convolutional subnetworks are used for decoupled coding, and a contrastive learning strategy is used to drive the attention map to focus on abnormal joints without frame‑level annotations. This is mainly achieved through the adaptive fusion mechanism to dynamically integrate the confidence of the deep network and the matching score of dynamic time warping template to maintain decision stability under conditions of pose degradation. Experiments on the NTU RGB+D and PKU‑MMD public datasets show that the action recognition accuracy of PDDS‑Net reaches 93.7%, the average precision of position‑level feedback is 0.656, 82.3% of the original AUC is still maintained at a 40% joint dropout rate, and the single‑frame inference latency is 41.8 ms, which meets the requirements of real-time interaction. This study provides a replicable technical path for the validation of rehabilitation evaluation algorithms without clinical data collection.