Predicting Task Intent From Surface Electromyography Using Layered Hidden Markov Models

Abstract

Human-robot interaction faces the challenge of designing and modeling tightly coupled and effectively controlled human-machine systems. This letter describes a method to learn human operator's performance characteristics from surface electromyography measurements to predict their intentions during task operations. For the first time, a layered hidden Markov model (LHMM) is successfully used with physiological data from cocontracting arm muscles to achieve accurate intent prediction. Furthermore, optimal model parameters and high-performing feature sets are identified and prediction accuracy at various time horizons calculated. The LHMM outperformed various other classification methods, including naive Bayes and support vector machine, ultimately achieving 82% accuracy in predicting the next 50 ms window of intent and maintaining 60% accuracy even after one second. These results hold the promise of improving robots' internal model of their human partners, which could increase the safety and productivity of human-robot teams in the factories of the future.