What question did this study set out to answer?

This research aims to address the challenges of predicting fall-related injuries using vision-based analysis, focusing on bridging visual and physical representations.

May 9, 2026Frontiers in Medicine0 citationsOpen Access

Bridging the visual-to-physical gap: physically aligned representations for fall risk analysis

XZXianqi Zhang XWXingtao Wang XFXiaopeng Fan

Key Points

This research aims to address the challenges of predicting fall-related injuries using vision-based analysis, focusing on bridging visual and physical representations.
Developed PHARL (PHysics-aware Alignment Representation Learning) framework for fall representation without requiring clinical labels.
Implemented two regularization mechanisms: trajectory-level temporal consistency and physics-aware alignment using simulation-derived outcomes.
Trained the model to capture dynamics in fall videos while allowing for a purely feed-forward inference process.
PHARL improved representation quality on four public datasets over visual-only baselines.
Demonstrated robust performance on fall detection tasks while utilizing no clinical outcome labels during training.
Learned representations exhibited an emergent ordinal structure without explicit supervision, suggesting effective underlying physics.

Abstract

Introduction Vision-based fall analysis has advanced rapidly, yet a key bottleneck remains: visually similar motions can correspond to markedly different physical outcomes, as subtle differences in contact mechanics and protective responses are difficult to infer from appearance alone. Existing approaches typically rely on supervised injury prediction, which requires reliable clinical labels. In practice, such labels are difficult to obtain due to ambiguity in video evidence (e.g., occlusion and viewpoint limitations) and the rarity and ethical constraints of real injury events, leading to noisy supervision. Methods We propose PHARL (PHysics-aware Alignment Representation Learning), a framework that learns physically meaningful fall representations without requiring clinical outcome labels. PHARL introduces two complementary regularization mechanisms: (1) trajectory-level temporal consistency to stabilize motion representations, and (2) physics-aware alignment, where simulation-derived multi-class contact outcomes are used to structure the embedding space. By associating video windows with temporally aligned simulation descriptors, the model captures local impact-relevant dynamics while maintaining a purely feed-forward inference process. Notably, physics information is only used during training as a structural regularizer and does not define an explicit outcome predictor. Results Experiments on four public datasets demonstrate that PHARL consistently improves risk-aligned representation quality compared to visual-only baselines, while maintaining strong performance on fall detection tasks. Discussion Beyond quantitative improvements, PHARL exhibits an emergent ordinal structure in the learned representation space, where an interpretable severity ordering (Head Trunk Supported) arises without explicit ordinal supervision. This suggests that physics-aware regularization can induce meaningful structural priors in representation learning, offering a promising direction for label-efficient fall analysis.

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