Predicting Task Intent From Surface Electromyography Using Layered Hidden Markov Models

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.