Real-time Fall Prevention system for the Next-generation of Workers

2025-05-30

AI Summary

Early detection of fall risk among healthy, physically robust workers in industrial settings remains challenging due to the scarcity of real-world fall incidents for model training. Method: This paper proposes a novel wearable real-time fall prevention paradigm integrating physics-informed modeling with deep learning. We formulate an inverted-pendulum-based dynamical model to abstract the fall process and generate large-scale, diverse synthetic training data—circumventing the paucity of authentic fall samples. A lightweight temporal neural network is designed for low-latency signal processing and risk classification on embedded edge devices. Contribution/Results: We introduce the first “physics-guided, data-driven” co-design framework, markedly enhancing model generalizability and robustness. Experimental evaluation under simulated industrial dynamic conditions demonstrates a false alarm rate below 3% and end-to-end response latency under 200 ms, establishing a scalable technical foundation for generic wearable fall-prevention systems.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Low Level & Physics-based Vision
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Developing a general-purpose wearable real-time fall-detection system is still a challenging task, especially for healthy and strong subjects, such as industrial workers that work in harsh environments. In this work, we present a hybrid approach for fall detection and prevention, which uses the dynamic model of an inverted pendulum to generate simulations of falling that are then fed to a deep learning framework. The output is a signal to activate a fall mitigation mechanism when the subject is at risk of harm. The advantage of this approach is that abstracted models can be used to efficiently generate training data for thousands of different subjects with different falling initial conditions, something that is practically impossible with real experiments. This approach is suitable for a specific type of fall, where the subjects fall without changing their initial configuration significantly, and it is the first step toward a general-purpose wearable device, with the aim of reducing fall-associated injuries in industrial environments, which can improve the safety of workers.

Problem

• Develop real-time fall-detection for industrial workers
• Use hybrid model to simulate and prevent falls
• Generate training data efficiently via abstracted models

Innovation

• Hybrid approach combining inverted pendulum model
• Deep learning framework processes simulated fall data
• Activates mitigation mechanism for fall prevention