Impulsive pattern recognition of a myoelectric hand via Dynamic Time Warping

Abstract

Although myoelectric prosthetic hands provide amputees with intuitive control, their reliance on many EMG sensors limits accessibility and makes them complex and expensive. To address this problem, this work presents a different perspective that makes use of a single EMG sensor and brief impulse signals in conjunction with Dynamic Time Warping (DTW) for accurate pattern detection. Conventional techniques rely on real-time data from multiple sensors, which can be costly and bulky. The method presents high accuracy while lowering hardware complexity and expense. A DTW-based system that reliably identifies muscle activation patterns from short EMG signals was created and tested. Results show that this single-sensor approach obtained an accuracy rate of 92%, which is similar to that of conventional multi-sensor systems. This research provides a more straightforward and economical approach that can be used to obtain enhanced myoelectric control. These findings provide a different perspective on more easily accessible and user-friendly prosthetic devices, which will be especially helpful in disaster-affected areas where quick deployment is essential. Future improvements would investigate this system's dependability over time and wider implementations in real situations, to take prosthetic technology one step further.