Dual-Stream Skeleton Feature Fusion for Fall Risk Assessment from Clinical Mobility Videos

Falls in the older population constitute a significant public health issue, motivating the development of automated fall risk assessment methods. However, existing approaches based on wearable sensors or depth cameras are often limited by hardware cost and deployment complexity. To this end, we present DualFRA, a vision-based deep learning approach that leverages standard cameras and integrates complementary mobility information from Timed Up and Go (TUG) and Five-Times Sit-to-Stand (FTSTS) test videos for comprehensive fall risk assessment. DualFRA first applies 3D pose estimation to extract skeletal sequences from the two clinical tests. For each sequence, multiple complementary representations are constructed, including a spatio-temporal skeletal graph and auxiliary views that emphasize joint configurations and motion dynamics. These representations are then processed by a spatio-temporal feature encoder combining self-attention-enhanced graph convolution and multi-scale temporal modeling to capture discriminative movement patterns. The resulting dual-stream features are integrated through cross-attention to produce a coherent semantic embedding for fall risk prediction. Experimental results averaged across multiple independent runs demonstrate that DualFRA attains accuracy and weighted-F 1 scores of 87.90% and 87.83%, surpassing baselines with average performance gains of 2.92% and 2.97%, respectively. Ablation analyses further confirm the benefits of the dual-stream design.