Assessment of Muscle Fatigue using a Probabilistic Framework for an EMG-based Robot Control Scenario

Human-robot control interfaces have received increased attention during the last decades. With the introduction of robots in every-day life, especially in developing services for people with special needs (i.e. elderly or impaired persons), there is a strong necessity of simple and natural control interfaces. In this paper, electromyographic (EMG) signals from muscles of the human upper limb are used as the control interface between the user and a robot arm. EMG signals are recorded using surface EMG electrodes placed on the user's skin, letting the user's upper limb free of bulky interface sensors or machinery usually found in conventional human-controlled systems. The proposed interface allows the user to control in real-time an anthropomorphic robot arm in three dimensional (3D) space, by decoding EMG signals to motion. However, since EMG changes due to muscle fatigue are present in this kind of control interface, a probabilistic framework has been developed, which can detect in real-time the muscle fatigue level. By complying to those fatigue-related signal changes, the proposed method can provide accurate decoding of motion through long periods of time. The system is used for the continuous control of a robot arm in 3D space, using only EMG signals from the upper limb. The method is tested for a long period of operation, proving that muscle fatigue does not affect the decoder accuracy. The efficiency of the method is assessed through real-time experiments including random arm motions in 3D space.