Studies on hand motion recognition based on biosignals have become popular as such recognition can be applied to various input interfaces and motion measurements for human–robot/computer interaction. In recent years, many machine-learning-based technologies have been developed to analyze such biosignals more accurately. Among various possible biosignals, we focus on forearm deformation which is an alternative source of information for hand motion recognition. The activities of surface and deep layer muscles, tendons, and bones can be extracted from forearm deformation in a non-invasive manner. In this study, a hand motion recognition system is proposed based on forearm deformation. By using machine-learning-based technology, the proposed method can be applied to various users and various measurement conditions. First, a distance sensor array is developed to measure forearm deformation. Then, we test and verify the suitableness of three types of machine-learning-based classifiers (k-NN, SVM, and DNN) using the measured forearm deformation. In experiments, we verified the accuracy of the proposed system with various users. We also test the system for different elbow postures, and when measuring the data over the clothing.

中文翻译：

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通过使用距离传感器阵列测量前臂变形的基于机器学习的手势识别系统

基于生物信号的手部动作识别研究已经变得很流行，因为这种识别可以应用于各种输入接口和用于人机/计算机交互的动作测量。近年来，已经开发了许多基于机器学习的技术来更准确地分析此类生物信号。在各种可能的生物信号中，我们关注前臂变形，这是手部动作识别的另一种信息来源。可以以无创方式从前臂变形中提取表面和深层肌肉，肌腱和骨骼的活动。在这项研究中，提出了一种基于前臂变形的手部运动识别系统。通过使用基于机器学习的技术，该方法可以应用于各种用户和各种测量条件。第一，开发了一种用于测量前臂变形的距离传感器阵列。然后，我们使用测得的前臂变形来测试和验证三种基于机器学习的分类器（k-NN，SVM和DNN）的适用性。在实验中，我们与不同的用户一起验证了所提出系统的准确性。我们还测试了系统的不同肘部姿势，以及在测量衣服上的数据时的情况。