Kamei Yuhei, Okada Shima
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:5680-5683. doi: 10.1109/EMBC.2016.7592016.
Robot arms for humanoid are widely developed for medical, welfare and education use. Surface electromyogram (sEMG) signals which are the electrical signals obtained from surface of human skin using electrodes have been mainly used for classification of hand motions. However, it is difficult to classify detailed motions such as finger motions and wrist pronation or supination. Moreover, Kinect is an integration sensor device which can capture human joints movement. It also has been widely used for recognition of body motions in many fields. However, it has some problems such as setting of camera and restriction of detection range. In this study, we propose an advanced method of motion classification by combining arm-shape-changes with sEMG to classify the detailed motions. Arm-shape-changes are forearm deformation caused by a bulge of muscle when subjects move an arm or a finger. Experimental results showed classification accuracies of 90% or more in wrist pronation and supination which are difficult to classify using only sEMG signals. As the result, our method could classify the detailed motions and contribute to expansion of classifiable hand motions.
用于类人机器人的机械臂已广泛应用于医疗、福利和教育领域。表面肌电图（sEMG）信号是使用电极从人体皮肤表面获取的电信号，主要用于手部动作分类。然而，很难对诸如手指动作以及手腕旋前或旋后等详细动作进行分类。此外，Kinect是一种能够捕捉人体关节运动的集成传感器设备，它也已在许多领域广泛用于身体动作识别。然而，它存在一些问题，如摄像头设置和检测范围受限。在本研究中，我们提出了一种通过将手臂形状变化与sEMG相结合来对详细动作进行分类的先进方法。手臂形状变化是指受试者移动手臂或手指时，由于肌肉隆起导致的前臂变形。实验结果表明，对于仅使用sEMG信号难以分类的手腕旋前和旋后动作，分类准确率达到90%或更高。因此，我们的方法能够对详细动作进行分类，并有助于扩大可分类的手部动作范围。
