Fatigue recognition using EMG signals and stochastic switched ARX model

The manmachine cooperative system is attracting great attention in many fields, such as industry, welfare, and so on. The assisting system must be designed so as to accommodate the operator's skill, which might be strongly affected by fatigue. This paper presents a new fatigue recognizer based on the electromyogram (EMG) signals and the stochastic switched ARX (SS-ARX) model which is one of the extended models of the standard hidden Markov model (HMM). Since the SS-ARX model can represent complex dynamic relationship which involves switching and stochastic variance, it is expected to show higher performance as a fatigue recognizer than when using simple statistical characteristics of the EMG signal and/or standard HMM. The usefulness of the proposed strategy is demonstrated by applying it to a peg-in-hole task. (c) 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.