Design of a wearable FMG sensing system for user intent detection during hand rehabilitation with a soft robotic glove

Abstract

This paper presents the design of a wearable feedback system based on force myography (FMG) for user intent detection during hand rehabilitation with a soft robotic glove. We present the development of a form-fitting FMG sensor band using force sensitive resistor (FSR). A supervised learning classifier, Artificial Neural Network (ANN), was implemented to classify four different hand motions with nearly instantaneous prediction speed. Experiments with three healthy subjects were devised to study the training speed and real-time classification accuracy. Results indicate an average training time of less than 95 seconds and a real time accuracy of approximately 95%. The study reveals the successful detection of four different hand motions and a high level of intuitive user intention-based control over the robotic glove.