Real-time estimation of thumb-tip forces using surface electromyogram for a novel human-machine interface

Abstract

Due to difficulties in measurement of muscle activities and understanding a user's intention under different configurations, controlling machine forces using surface electromyogram (SEMG) is difficult in a human-machine interface (HMI). This study describes a novel HMI using Hill-based muscle model to control the isometric force of a robotic thumb that considers the importance of the thumb in hand function. In order to estimate force intension, SEMG from the skin surface was measured and converted to muscle activation information. The activations of deep muscles were inferred from the ratios of muscle activations from earlier studies. The muscle length of each contributed muscle was obtained by using a motion capture system and musculoskeletal modeling software packages. Once muscle forces were calculated, thumb-tip force was estimated based on a mapping model from the muscle force to thumb-tip force. The proposed method was evaluated in comparisons with a linear regression and artificial neural network (ANN) under four different thumb configurations to investigate the potential for estimations under conditions in which the thumb configuration changes.