Unsupervised Keypoints for Real-Time Fall Detection: Comparative Analysis Under Real-world Conditions with Predictive Bandwidth Reduction

2026-07-16

AI Summary

This work addresses the deployment challenges of real-time fall detection for older adults—particularly privacy concerns, computational overhead, and bandwidth constraints—and tackles the significant performance degradation of supervised keypoint-based methods under occlusion or low-visibility conditions. To this end, we propose a privacy-preserving fall detection framework leveraging unsupervised keypoints extracted locally, combined with a variational recurrent neural network for motion sequence prediction. Fall events are classified at the sequence level by measuring discrepancies between observed and predicted motion. We present the first systematic comparison of supervised versus unsupervised keypoints in real-world fall detection, demonstrating that the latter exhibits markedly superior robustness under occlusion and subject invisibility. Additionally, we introduce a prediction-driven bandwidth compression mechanism. Experiments on the UR Fall Detection and Human Fall datasets show that our method substantially outperforms supervised approaches in occluded scenarios—where the latter miss nearly 50% of falls—with even greater advantages under bandwidth limitations.

Abstract

Falls among older adults are a major safety challenge, but continuous monitoring is difficult to sustain. Video captures fall-related posture and motion, yet deployment is limited by privacy, computation, and bandwidth. Supervised pose estimation is anatomically interpretable but vulnerable to occlusion and partial body visibility. We propose a privacy-preserving framework that replaces RGB transmission with compact motion representations based on unsupervised keypoints and predictive temporal modeling. Local processing performs segmentation and keypoint extraction; variational recurrent prediction and sequence classification then detect falls from observed and forecasted motion. We evaluate the framework on the UR Fall Detection and Human Fall datasets using random, subject-disjoint, and occlusion-based splits. Under random splits, neither representation consistently dominates, suggesting that standard protocols may hide meaningful differences. Under subject-disjoint evaluation, supervised keypoints show a statistically significant advantage, but performance varies by subject: they perform better when anatomical landmarks are visible, whereas unsupervised keypoints are more robust to occlusion and partial visibility, though they produce more false positives for complex activities. Under occlusion-based evaluation, supervised keypoints miss nearly half of all falls, while unsupervised keypoints retain strong sensitivity and substantially outperform them. Their anatomical independence allows spatial anchors to adapt to visible body structure rather than fail on absent landmarks. The gap widens under bandwidth constraints, where supervised localization errors compound through the temporal model. These findings show that representation choice should reflect expected visual conditions and that unsupervised keypoints offer an advantage when body visibility is compromised.

Problem

• fall detection
• occlusion
• unsupervised keypoints
• bandwidth constraints
• privacy-preserving

Innovation

• unsupervised keypoints
• fall detection
• occlusion robustness
• bandwidth reduction
• privacy-preserving video analysis