Extraction of elbow joint intention from surface EMG signals in horizontal plane

A human-robot interaction (HRI) system could be more effective if it is intelligent enough to accurately measure and predict humanspsila moving intention and adapt their behaviors accordingly. By means of measuring biomechanical signals of human body to make such predication has been a challenging research topic. This paper presents results of our investigation to extract humanpsilas elbow joint motion intention from surface electromyographic (sEMG) measurement. While a human subjectpsilas limb is kept still on a horizontal table but subject to certain force applied to the hand, by changing the direction of the force applied, the biomechanical signals are simultaneously recorded from three sEMGs mounted on superficial elbow and shoulder muscles in real-time. Based on the measurement obtained, using of cosine tuning functions, we study the relation between static sEMG signals and movement trends in eight directions of the elbow. The main contribution of this paper is that we investigate the relationship between the EMG signal and the motion intention direction in a horizontal plane using cosine tuning functions with 2^pi radians.