Real-time Fall Prevention system for the Next-generation of Workers
This work addresses fall prevention for industrial workers, but it is incremental as it focuses on a specific type of fall and is a first step toward a general-purpose device.
The paper tackles real-time fall prevention for industrial workers by combining an inverted pendulum model with deep learning to simulate falls and trigger mitigation mechanisms, aiming to reduce injuries in harsh environments.
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.