Leveraging Spatial-Temporal-Scale Augmentation to Assess Motor Abilities in Stroke Patients

Assessing motor abilities is crucial for the rehabilitation of post-stroke patients. However, in practical scenarios, medical data often suffer from issues like limited sample size and low data quality. Existing motion recognition methods struggle to learn generalizable features from limited low-quality data, resulting in poor model generalization and low recognition accuracy. Therefore, this paper proposes a spatial-temporal-scale augmentation-based motor ability assessment method (named STSAug). STSAug consists of three parts: a Spatial-Temporal Feature Extractor (STFE), Skeletal Spatial Augmentation (SSA), and Temporal Augmentation based on Contrastive Learning (TA-CL). STFE is designed to extract spatio-temporal features from noisy skeletal point data, mitigating the interference caused by the noise. SSA enhances spatial features of skeletal points that are relevant to classification while minimizing the impact of irrelevant skeletal features on the model. TA-CL applies temporal augmentation and utilizes contrastive learning to enable the model to learn time-independent features, thereby improving the model’s generalization capability. We evaluated our method on the public dataset NTU RGB+D 60 and our own collected dataset of real stroke patient motion data to verify its effectiveness. The experimental results demonstrate that our proposed STSAug possesses a strong ability to extract spatio-temporal generalization features in real scenarios and achieves the best motor ability recognition performance.