Na Youngjin, Kim Sangjoon J, Jo Sungho, Kim Jung
Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
School of Computing, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Med Biol Eng Comput. 2017 Aug;55(8):1507-1518. doi: 10.1007/s11517-016-1608-4. Epub 2017 Jan 4.
Previous pattern recognition algorithms using surface electromyography (sEMG) have been developed for subsets of predefined hand movements without considering muscle structure. In order to decode hand movements, it is important to know which movements are appropriate for PR due to the different independence of movements between individuals and the high correlated characteristics of sEMG patterns between movements. This paper proposes a method to personally rank the order of hand movements from subsets (31 finger flexion, 31 finger extension, and 4 wrist movements in this paper). The movements were sorted into a ranked order with respect to the locations of the electrodes on the proximal forearm and the distal forearm. We evaluated the classification error as the number of desired movements (N ) changed. The maximum N with an error lower than 10% was 20 for the proximal forearm and 10 for the distal forearm from ranked movements of individuals. Our method could help to identify the optimized order of hand movements considering the personal characteristics of each individual.
先前使用表面肌电图（sEMG）的模式识别算法是针对预定义手部动作的子集开发的，未考虑肌肉结构。为了解码手部动作，由于个体之间动作的独立性不同以及动作之间sEMG模式的高度相关特性，了解哪些动作适合模式识别非常重要。本文提出了一种方法，用于对来自子集（本文中有31种手指弯曲、31种手指伸展和4种腕部动作）的手部动作顺序进行个人排序。根据电极在前臂近端和远端的位置，将这些动作按顺序排列。我们评估了随着期望动作数量（N）的变化分类误差。从个体的排序动作来看，对于前臂近端，误差低于10%时的最大N为20，对于前臂远端则为10。我们的方法有助于考虑每个个体的个人特征来识别手部动作的优化顺序。
