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

TL;DR

This paper introduces a hybrid fall detection and prevention system using dynamic modeling and deep learning to generate training data and activate mitigation mechanisms, aiming to improve industrial worker safety.

Contribution

It presents a novel hybrid approach combining dynamic models and deep learning for real-time fall detection and prevention in industrial settings.

Findings

Effective simulation of falls for training data generation

Potential for real-time fall mitigation activation

First step towards general-purpose wearable fall prevention devices

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.