Design and testing of an sEMG based bellow-the-elbow prosthetic
Session Number
1
Advisor(s)
Mark Carlson, IMSA
Location
A131
Discipline
Engineering
Start Date
15-4-2026 10:15 AM
End Date
15-4-2026 11:00 AM
Abstract
Below-the-elbow prosthetics are expensive and offer limited reliability. Most use surface electromyography for prosthetic control due to its non-invasive nature. However, current systems require up to eight sensors, increasing cost and power consumption. Alternatively, this underactuated prosthetic design uses six sEMG placed on major muscle groups, mitigating signal degradation arising from variations in body size, movement, perspiration, etc. A bandpass filter tuned to 10–150 Hz excluded spurious extreme frequencies due to movement and environmental signals. Standard Butterworth filter topology was tuned in KiCAD simulation software, using LTSpice simulation settings. Signals were processed remotely, using pattern recognition to refine motions while lowering weight and power required. We successfully tested a bellow-the-elbow prosthetic arm integrated with sEMG’s along major flexor and extensor muscles. Our sEMGs sensed expected physiological depolarization (+100 millivolts from baseline) in relevant muscle groups during activity, and displayed minimum movement and drift during extended tests. Battery life depended on user activity, ranging from 4-8 hours in our tests. Noise processing was successful in cutting out extraneous noise, and further work is being done to quantify signal-to-noise ratio. Further work may focus on viability of a second stage vs. a four stage Butterworth filter.
Design and testing of an sEMG based bellow-the-elbow prosthetic
A131
Below-the-elbow prosthetics are expensive and offer limited reliability. Most use surface electromyography for prosthetic control due to its non-invasive nature. However, current systems require up to eight sensors, increasing cost and power consumption. Alternatively, this underactuated prosthetic design uses six sEMG placed on major muscle groups, mitigating signal degradation arising from variations in body size, movement, perspiration, etc. A bandpass filter tuned to 10–150 Hz excluded spurious extreme frequencies due to movement and environmental signals. Standard Butterworth filter topology was tuned in KiCAD simulation software, using LTSpice simulation settings. Signals were processed remotely, using pattern recognition to refine motions while lowering weight and power required. We successfully tested a bellow-the-elbow prosthetic arm integrated with sEMG’s along major flexor and extensor muscles. Our sEMGs sensed expected physiological depolarization (+100 millivolts from baseline) in relevant muscle groups during activity, and displayed minimum movement and drift during extended tests. Battery life depended on user activity, ranging from 4-8 hours in our tests. Noise processing was successful in cutting out extraneous noise, and further work is being done to quantify signal-to-noise ratio. Further work may focus on viability of a second stage vs. a four stage Butterworth filter.