# Dual-Stream STGCN with Motion-Aware Grouping for Rehabilitation Action Quality Assessment

## TL;DR

This paper introduces a new method for assessing the quality of rehabilitation exercises using motion capture data and joint grouping strategies.

## Contribution

The novel contribution is a dynamic, motion-aware joint grouping strategy integrated into a dual-stream neural network for action quality assessment.

## Key findings

- The proposed method reduces mean absolute deviation by 26.5% on the KIMORE dataset compared to existing methods.
- Dynamic joint grouping and two-stream design significantly improve action quality assessment performance.
- The model achieves best scores on most exercises in KIMORE and remains competitive on UI-PRMD.

## Abstract

Action quality assessment automates the evaluation of human movement proficiency, which is vital for applications like sports training and rehabilitation, where objective feedback enhances patient outcomes. Action quality assessment processes motion capture data to generate quality scores for action execution. In rehabilitation exercises, joints typically work synergistically in functional groups. However, existing methods struggle to accurately model the collaborative relationships between joints. Fixed joint grouping is not flexible enough, while fully adaptive grouping lacks the guidance of prior knowledge. In this paper, based on rehabilitation theory in clinical medicine, we propose a dynamic, motion-aware grouping strategy. A two-stream architecture independently processes joint position and orientation information. Fused features are adaptively clustered into 6 functional groups by a joint motion energy-driven learnable mask generator, and intra-group temporal modeling and inter-group spatial projection are achieved through two-stage attention interaction. Our method achieves competitive results and obtains the best scores on most exercises of KIMORE, while remaining comparable on UI-PRMD. Experimental results using the KIMORE dataset show that the model outperforms current methods by reducing the mean absolute deviation by 26.5%. Ablation studies validate the necessity of dynamic grouping and the two-stream design. The core design principles of this study can be extended to fine-grained action-understanding tasks such as surgical operation assessment and motor skill quantification.