用轻型外骨骼结合肌电与运动数据，实时估算手部开合状态和柔顺度。
Learning Hand State Estimation for a Light Exoskeleton
- 融合前臂肌电与外骨骼运动信息，构建监督学习估计算法。
- 单用户训练后跨会话测试仍具良好预测性能。
- 适合居家康复场景，支持自适应控制与疗效评估。
我们提出一种基于机器学习的手部状态估计算法，用于康复目的，采用轻型外骨骼设备。这类设备便于使用，适合开展家庭化、高频次的康复治疗。该方法利用前臂肌电活动与外骨骼运动信息，重建手部开合程度和柔顺水平。这些信息可用于评估治疗进展并实现自适应控制。我们在真实轻型外骨骼上验证了该方法，实验表明：在仅由单一用户数据训练并跨会话测试时，系统仍表现出良好的预测性能。这一泛化能力使本系统在实际康复应用中具有广阔前景。
原文摘要 · Abstract (English)
We propose a machine learning-based estimator of the hand state for rehabilitation purposes, using light exoskeletons. These devices are easy to use and useful for delivering domestic and frequent therapies. We build a supervised approach using information from the muscular activity of the forearm and the motion of the exoskeleton to reconstruct the hand's opening degree and compliance level. Such information can be used to evaluate the therapy progress and develop adaptive control behaviors. Our approach is validated with a real light exoskeleton. The experiments demonstrate good predictive performance of our approach when trained on data coming from a single user and tested on the same user, even across different sessions. This generalization capability makes our system promising for practical use in real rehabilitation.
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