用倒立摆模拟生成数据，实现工业工人实时防跌倒预警。
Real-time Fall Prevention system for the Next-generation of Workers
- 通过倒立摆模型生成多样跌倒场景数据，替代真实实验。
- 在模拟数据上训练深度学习模型，实现毫秒级风险识别。
- 适合工业场景下强健工人的跌倒预防，为可穿戴设备奠基。
开发通用的可穿戴实时跌倒检测系统仍是挑战，尤其针对健康强壮的工业工人。本文提出一种混合方法：利用倒立摆动态模型生成跌倒模拟数据，输入深度学习框架，输出信号以触发跌倒缓解机制。该方法优势在于，抽象模型可高效生成数千种不同初始条件下的跌倒数据，而真实实验难以实现。此方案适用于初始姿态变化不大的特定类型跌倒，是迈向通用可穿戴防跌设备的第一步，旨在降低工业环境中的跌倒伤害，提升工人安全。
原文摘要 · Abstract (English)
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.
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