Classification of Differential Bimanual Manuel Motion

There are various fields in which, knowledge of arm movement can help improve the lives of people. These include physical therapy, replacement robots and sports science. Understanding the neural signals that can be found in the course of a motion will add an extra dimension in the quest to classify these types of motion. Bimanual motion is movement of the two hands. The classification of hand movements has been addressed in different ways. This project looks into the possibility of using Electromyography data obtained using a Myo Armband to classify motions. Experiments were designed to break down several arm movements from the elbow up, in an attempt to classify them. Human participants performed several motions under set conditions. This project undertook data from some sample movements performed, and sought to classify the said motion using the analog electromyography data obtained from electrodes in the form of an armband placed on the subjects forearm. The data obtained using this process for various subjects was analyzed to arrive at the conclusions regarding this process of classification. Computer software was used in helping analyze the results of the said process. The presentation presents the results and conclusions obtained during the course of the experiment. It also looks into the possibility of future use of the information gathered regarding classification of motion.