Interpretation of Surface Electromyograms to Characterize Arm Movement

Abstract

& In this investigation, surface electromyogram signals were studied from muscles above the elbow to characterize operations of the arm. The instrumentation consisted of surface electrodes, signal acquisition protocols, and signal conditioning at different levels. A program code was written and used to acquire the signal from the hardware, and interpretation was done to estimate parameters using a simulated algorithm. Different types of arm operations were analyzed using one way repeated factorial analysis of variance to justify the use of the surface electromyogram signal to identify arm motions. The results also present operations that may be easily realized by prosthetic devices. This work provides fundamental information to develop more powerful, flexible, and efficient prosthetic design.

Development of prosthetic arm using body actioned SEMG signals

“…Since noninvasive electrodes (planted in this current research) are required to pick up signals to stimulate muscles for feeding information back through bio-feedback system so necessary steps for ampli¯cation, processing and digitalization within the framework of these endeavors were exercised for generating the utilized SEMG signal to perform various prosthesis activities. 17,18 Now, one could easily utilize these signals (S2/S4: strong contraction signal), to open or to close function and vice versa. In addition, to grasp hold function, another signal of S3 is required, if hand is in open state.…”

Analysis and recognition of operations using SEMG from upper arm muscles

“…For the second activity (P2), the arm was moved upside and the position is called flexion elbow. In the third activity (P3), arm was rotated in abduction direction, and finally, for the fourth activity (P4), arm was rotated in adduction direction (Veer, ).…”

“…Electrical signals produced by various bodily activities are becoming increasingly important in many applications, including monitoring/diagnosis, human computer interactions, and more (Atzori et al, ; Farina et al, ; Garcia, Okuno, & Akazawa, ; Parker, Englehart, & Hudgins, ; Tang, Liu, Lv, & Sun, ; Veer, ; Veer, ). The surface electromyogram (SEMG) signals have been widely applied in the field of prosthetic design, control, pattern recognition, muscle fatigue analysis, and others.…”