Fusing Biomechanical and Spatio-Temporal Features for Fall Prediction: Characterizing and Mitigating the Simulation-to-Reality Gap

2025-11-18

AI Summary

A significant performance gap exists in fall prediction for elderly individuals—particularly those with diabetes or frailty—between simulation and real-world settings, with zero-shot transfer F1 scores plummeting from 89.0% to 35.9%. Method: We propose BioST-GCN, a dual-stream graph convolutional network integrating biomechanical priors and spatiotemporal graph structures; it employs cross-modal attention for adaptive fusion of pose and biomechanical features, and introduces spatiotemporal attention to localize critical joints and motion phases—enhancing interpretability—alongside a personalized adaptation strategy to mitigate domain shift. Contribution/Results: Trained exclusively on MCF-UA and MUVIM simulation data, BioST-GCN achieves substantially improved simulation-to-reality generalization under non-invasive constraints. Our results underscore the necessity of personalized modeling and real-data closed-loop validation for clinical deployment.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Computer Vision: Biometrics, Face, Gesture & Pose

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Falls are a leading cause of injury and loss of independence among older adults. Vision-based fall prediction systems offer a non-invasive solution to anticipate falls seconds before impact, but their development is hindered by the scarcity of available fall data. Contributing to these efforts, this study proposes the Biomechanical Spatio-Temporal Graph Convolutional Network (BioST-GCN), a dual-stream model that combines both pose and biomechanical information using a cross-attention fusion mechanism. Our model outperforms the vanilla ST-GCN baseline by 5.32% and 2.91% F1-score on the simulated MCF-UA stunt-actor and MUVIM datasets, respectively. The spatio-temporal attention mechanisms in the ST-GCN stream also provide interpretability by identifying critical joints and temporal phases. However, a critical simulation-reality gap persists. While our model achieves an 89.0% F1-score with full supervision on simulated data, zero-shot generalization to unseen subjects drops to 35.9%. This performance decline is likely due to biases in simulated data, such as `intent-to-fall' cues. For older adults, particularly those with diabetes or frailty, this gap is exacerbated by their unique kinematic profiles. To address this, we propose personalization strategies and advocate for privacy-preserving data pipelines to enable real-world validation. Our findings underscore the urgent need to bridge the gap between simulated and real-world data to develop effective fall prediction systems for vulnerable elderly populations.

Problem

• Predicting falls in older adults using biomechanical and spatio-temporal features
• Addressing the simulation-to-reality gap in fall prediction systems
• Improving generalization through personalization and privacy-preserving data

Innovation

• Dual-stream model fuses pose and biomechanical features
• Uses cross-attention mechanism for feature integration
• Proposes personalization strategies for simulation-reality gap