The upper limb of the body is a vital for various kind of activities for human. The complete or partial loss of the upper limb would lead to a significant impact on daily activities of the amputees. EMG carries important information of human physique which helps to decode the various functionalities of human arm. EMG signal based bionics and prosthesis have gained huge research attention over the past decade. Conventional EMG-PR based prosthesis struggles to give accurate performance due to off-line training used and incapability to compensate for electrode position shift and change in arm position. This work proposes online training and incremental learning based system for upper limb prosthetic application. This system consists of ADS1298 as AFE (analog front end) and a 32 bit arm cortex-m4 processor for DSP (digital signal processing). The system has been tested for both intact and amputated subjects. Time derivative moment based features have been implemented and utilized for effective pattern classification. Initially, system have been trained for four classes using the on-line training process later on the number of classes have been incremented on user demand till eleven, and system performance has been evaluated. The system yielded a completion rate of 100% for healthy and amputated subjects when four motions have been considered. Further 94.33% and 92% completion rate have been showcased by the system when the number of classes increased to eleven for healthy and amputees respectively. The motion efficacy test is also evaluated for all the subjects. The highest efficacy rate of 91.23% and 88.64% are observed for intact and amputated subjects respectively.

翻译：人体上肢对于各种活动至关重要。上肢完全或部分缺失会对截肢者的日常生活造成显著影响。肌电信号（EMG）承载着人体生理的重要信息，有助于解码人臂的各种功能。基于EMG信号的仿生学与假肢技术在过去十年中获得了大量研究关注。传统基于EMG模式识别的假肢由于采用离线训练，且无法补偿电极位置偏移和手臂位置变化，难以实现精准控制。本研究提出一种基于在线训练与增量学习的上肢假肢控制系统。该系统采用ADS1298作为模拟前端（AFE），并配备32位Arm Cortex-M4处理器进行数字信号处理（DSP）。系统已在健全受试者和截肢受试者身上进行了测试。采用基于时间导数矩的特征进行有效的模式分类。系统最初通过在线训练过程对四个动作类别进行训练，随后根据用户需求将类别数逐步增至十一类，并评估系统性能。当处理四个动作时，系统在健全受试者和截肢受试者上的完成率达到100%。当类别增至十一类时，系统在健全受试者和截肢受试者上的完成率分别为94.33%和92%。同时对所有受试者进行了运动效能测试，健全受试者和截肢受试者的最高效能率分别为91.23%和88.64%。