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.

翻译：我们提出了一种基于机器学习的手部状态估计器，用于康复目的，采用轻型外骨骼。这些设备易于使用，有助于在家庭环境中进行频繁的治疗。我们构建了一种监督式方法，利用前臂肌肉活动信息和外骨骼的运动信息，来重建手部的张开程度和顺应性水平。此类信息可用于评估治疗进展并开发自适应控制行为。我们的方法在一个真实的轻型外骨骼上得到了验证。实验表明，当使用来自单个用户的数据进行训练并在同一用户（即使跨越不同会话）上进行测试时，我们的方法具有良好的预测性能。这种泛化能力使我们的系统在实际康复应用中具有广阔前景。