sEMG-Based Classification Strategy of Hand Gestures for Wearable Robotics in Clinical Practice

Abstract

Every day more and more robotic aids, of different shapes, sizes, and functions, enter the clinics to take part in the rehabilitative and assistive paths for patients with reduced mobility. Accompanying discharged patients with robotics-based remote rehabilitation and home assistance seems to be one of the most promising avenues to follow to increase the success rate of these practices and lighten the overall burden on national health systems. However, to get out of clinics effectively, robotics must become wearable and, therefore, based on the use of embedded low-power electronics, both for practicality and safety reasons. The point is further complicated when it comes to assisting or rehabilitating lost hand functionalities due to the small size and complex mobility of such a limb; moreover, ensuring the real-time execution of gesture classification algorithms for controlling these devices hence becomes a vital engineering challenge. A hand gesture classification solution, specifically designed for the implementation of embedded electronics, based on surface electromyography, and ensuring real-time action, will be presented in this paper.

The authors would like to thank RING@LAB (joint laboratory between the University of Florence and the Don Carlo Gnocchi Foundation) for logistic and clinical support to the research, and the University of Florence that funded this work through the HOLD project.