用肌电数据预测工人手腕动作和用力，帮工业外骨骼更精准护腕。
How can AI reduce wrist injuries in the workplace?
- 通过8通道肌电传感器识别手腕动作模式
- 建立肌电与用力强度的回归模型，准确率超90%
- 适合制造业外骨骼研发人员参考
本文研究了一种工业可穿戴手腕外骨骼的控制与传感策略，通过分类和预测工人动作来降低腕部损伤风险。基于六名健康受试者在制造厂采集的数据，构建了基于肌电（EMG）的动作分类模型和力值预测模型。使用8通道肌电传感器（Myo Armband）获取表面肌电信号实现手腕运动识别；利用商用手持式测力仪（Vernier GoDirect Hand Dynamometer）采集手腕与手部用力数据，建立力值回归模型。该控制策略为工业应用外骨骼设计提供基础，强调结构简化、成本降低及最少传感器使用，同时确保辅助的可靠性与有效性。
原文摘要 · Abstract (English)
This paper explores the development of a control and sensor strategy for an industrial wearable wrist exoskeleton by classifying and predicting workers' actions. The study evaluates the correlation between exerted force and effort intensity, along with sensor strategy optimization, for designing purposes. Using data from six healthy subjects in a manufacturing plant, this paper presents EMG-based models for wrist motion classification and force prediction. Wrist motion recognition is achieved through a pattern recognition algorithm developed with surface EMG data from an 8-channel EMG sensor (Myo Armband); while a force regression model uses wrist and hand force measurements from a commercial handheld dynamometer (Vernier GoDirect Hand Dynamometer). This control strategy forms the foundation for a streamlined exoskeleton architecture designed for industrial applications, focusing on simplicity, reduced costs, and minimal sensor use while ensuring reliable and effective assistance.
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