A Force Myography based HMI for Classification of Upper Extremity Gestures

Abstract

Advancement in the field of rehabilitation has led to develop state-of-art multi-dexterous robotic hands such that to restore Activities of Daily Livings (ADLs) of upper limb amputees. However, these high-tech devices require an effective human-machine interface (HMI) for conversion of musculotendinous activities to myoelectric signals for control and functioning of robotic hands. In this study, a novel force myography (FMG) based HMI, considered as a potential alternate to sEMG, was developed. FMG band having five resistive based pressure sensors was developed for monitoring of change in stiffness of muscles during gestures. This flexible, un-stretchable, and adjustable FMG band is capable to be fastened on any adult forearm regardless of the size and shape of forearm. Voltage divider circuit was used to extract signals from FMG band. Five intact subjects participated in this study and protocol was developed for prediction of five static gestures such as relax, power, precision, supination, and pronation. All of subjects recorded selected gestures for three times. Gestures were classified using linear discriminant analysis (LDA) and support vector machines (SVM). SVM shows higher classification accuracy than LDA. LDA and SVM demonstrated prediction accuracies upto 87.2% and 93.3%, respectively.