中文摘要：

将肌音（Mechanomyography,MMG）信号作为假肢控制的生理信号源,实现了对于虚拟假肢的抓放控制。针对手部在握紧-张开动作过程中前臂肌肉声音信号,提取动作信号的7种时域特征并利用线性分类器进行分类识别,用以分辨手部动作类型,正确率为（95.63±2.55）%,并利用辨识结果产生控制信号实现对虚拟手的控制。结果表明肌音信号的动作判断具有很高的正确率,为利用肌音信号控制假肢提供了依据。

英文摘要：

A novel way was presented to drive a virtual hand by using mechanomyography（MMG） signal. The MMG signal of the hand open grip motion is acquired from the forearm, and seven kinds of time-domain features were extracted. Linear discriminant analysis was used for hand motion modes recognition. The final classification accuracy of motion modes is （95. 63±2. 55）%. Motion recognition results were utilized to generate proper pulses to manipulate a virtual prosthesis. The results show that the MMG signal has high accuracy of judging movements, and provides basis for prosthetic control of using MMG signal.