The control of a prosthetic arm by EMG pattern recognition

An electromyographic (EMG) signal pattern recognition system is constructed for real-time control of a prosthetic arm through precise identification of motion and speed command. A probabilistic model of the EMG patterns is first formulated in the feature space of integral absolute value (IAV). Then, the sample probability density function of pattern classes in the feature space of variance and zero crossings is derived for classification based on this model and the relations between IAV, variance and zero crossings. A multiclass sequential decision procedure is designed for pattern classification with the emphasis on computational simplicity. The upper bound of probability of error and the average number of sample observations are investigated. Speed and motion predictions incorporate with decision procedure to enhance the decision speed and reliability. A decomposition rule is formulated for the direct assignment of speed to each primitive motion involved in a combined motion. Learning procedure is designed for the decision processor to adapt long-term pattern variation. The overall procedure is explained as an application of hierachically intelligent control system theory. Experimental results verify the effectiveness of the proposed theories and procedures.