One challenge of physical human robot interaction has been modelling tightly coupled human-machine systems to design effective controllers for them. The objective of this research is to learn human operators performance character- istics 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 co-contracting 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 1 sec. 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.