  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.
