Two-stage posture enhancement and stability modelling for pre-fall risk detection in athletic movements

In sports training and athletic performance, fall risk degrades performance and elevates injury incidence, making early warning of pre-fall states critical. Monocular 3D pose estimation often yields incomplete or biomechanically inconsistent skeletons due to occlusion, blur, and detector noise, limiting reliable monitoring of movement stability and quality.

We propose a two-stage deep learning framework: 1. RePoseNet: Refines initially estimated 3D skeleton sequences generated by a pretrained monocular pose estimator, rather than performing monocular 3D pose estimation from scratch. It repairs missing or unreliable joints, suppresses temporal jitter, and enforces biomechanical plausibility through joint symmetry, limb-length consistency, and joint-angle constraints. 2. StaFallNet: Integrates stability metrics-Step Frequency Variation Rate (SFVR), Trunk Leaning Angle (TLA), and Stride Variability (SV)-into a dynamic graph network to model spatial coordination and temporal dynamics for frame-level and event-level pre-fall risk prediction.

RePoseNet combines GRU-based temporal repair, cross-joint attention, and biomechanical refinement (limb symmetry, joint-angle limits). StaFallNet extracts biomechanical features (SFVR, TLA, SV), constructs dynamic joint graphs modulated by stability cues, and uses attention pooling for risk classification. Evaluation: Tested on a fall-related subset of UCF101 with manually annotated frame-level labels. Pre-fall frames were defined as frames within a fixed temporal window before the visually identified fall onset, while non-risk frames were sampled from stable movement periods.

RePoseNet improves temporal smoothness and biomechanical plausibility under controlled skeleton-level corruption. Reference-based Mean Per Joint Position Error and PCK evaluations indicate that the refined skeletons remain consistent with the initial pseudo-3D pose estimates while reducing temporal instability; these metrics are not interpreted as evidence of improved absolute 3D pose accuracy. More importantly, downstream experiments demonstrate that the refined skeletons provide more reliable inputs for stability-aware fall-risk prediction.

This work offers a robust, interpretable approach for fall-risk prediction in sports. It provides actionable insights for performance evaluation, training intervention, and injury prevention, bridging computational modeling with biomechanical principles.