Yoshikawa Masahiro, Baba Kohei, Ogawa Kazunori, Takamatsu Jun, Ogasawara Tsukasa
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:4955-4958. doi: 10.1109/EMBC.2016.7591839.
Studies of upper limb motion analysis using surface electromyogram (sEMG) signals measured from the forearm plays an important role in various applications, such as human interfaces for controlling robotic exoskeletons, prosthetic hands, and evaluation of body functions. Though the sEMG signals have a lot of information about the activities of the muscles, the signals do not have the activities of the deep layer muscles. We focused on forearm deformation, since hand motion brings the muscles, tendons, and skeletons under the skin. The reason why we focus is that we believe the forearm deformation delivers information about the activities of deep layer muscles. In this paper, we propose a hand motion recognition method based on the forearm deformation measured with a distance sensor array. The method uses the support vector machine. Our method achieved a mean accuracy of 92.6% for seven hand motions. Because the accuracy of the pronation and the supination are high, the distance sensor array has the potential to estimate the activities of deep layer muscles.
利用从前臂测量的表面肌电图（sEMG）信号进行上肢运动分析的研究在各种应用中发挥着重要作用，例如用于控制机器人外骨骼、假肢手的人机接口以及身体功能评估。尽管sEMG信号包含了大量有关肌肉活动的信息，但这些信号并未包含深层肌肉的活动信息。我们关注前臂变形，因为手部运动带动了皮肤下的肌肉、肌腱和骨骼。我们关注的原因是我们认为前臂变形传递了有关深层肌肉活动的信息。在本文中，我们提出了一种基于距离传感器阵列测量的前臂变形的手部运动识别方法。该方法使用支持向量机。我们的方法对七种手部运动实现了92.6%的平均准确率。由于旋前和旋后的准确率较高，距离传感器阵列有潜力估计深层肌肉的活动。
