The ability to predict wrist and hand motions simultaneously is essential for natural controls of hand protheses. In this paper, we propose a novel method that includes subclass discriminant analysis (SDA) and principal component analysis for the simultaneous prediction of wrist rotation (pronation/supination) and finger gestures using wearable ultrasound. We tested the method on eight finger gestures with concurrent wrist rotations. Results showed that SDA was able to achieve accurate classification of both finger gestures and wrist rotations under dynamic wrist rotations. When grouping the wrist rotations into three subclasses, about 99.2 ± 1.2% of finger gestures and 92.8 ± 1.4% of wrist rotations can be accurately classified. Moreover, we found that the first principal component (PC1) of the selected ultrasound features was linear to the wrist rotation angle regardless of finger gestures. We further used PC1 in an online tracking task for continuous wrist control and demonstrated that a wrist tracking precision ( ${R}^{{2}}$ ) of 0.954 ± 0.012 and a finger gesture classification accuracy of 96.5 ± 1.7% can be simultaneously achieved, with only two minutes of user training. Our proposed simultaneous wrist/hand control scheme is training-efficient and robust, paving the way for musculature-driven artificial hand control and rehabilitation treatment.

中文翻译：

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通过穿戴式超声感应同时预测手腕/手部动作

同时预测手腕和手部动作的能力对于自然控制手部假肢至关重要。在本文中，我们提出了一种新颖的方法，该方法包括子类判别分析（SDA）和主成分分析，可使用可穿戴超声同时预测手腕旋转（旋前/旋后）和手指手势。我们在手腕同时旋转的八个手指手势上测试了该方法。结果表明，SDA能够在动态手腕旋转下实现手指手势和手腕旋转的准确分类。将腕部旋转分为三个子类时，可以准确地将约99.2±1.2％的手指手势和92.8±1.4％的腕部旋转准确分类。此外，我们发现所选超声特征的第一个主成分（PC1）与手腕旋转角度呈线性关系，而与手指手势无关。我们进一步将PC1用于在线跟踪任务中以实现连续的手腕控制，并证明了手腕跟踪的精度（ $ {R} ^ {{2}} $ 只需两分钟的用户培训，即可同时获得0.954±0.012的）和96.5±1.7％的手指手势分类精度。我们提出的手腕/手同时控制方案是训练有效且健壮的，为肌肉组织驱动的人工手控制和康复治疗铺平了道路。