Hand gesture recognition using force myography of the forearm activities and optimized features
Proceedings of the IEEE International Conference on Industrial Technology
Feature extraction, Force Myography, Hand gestures, Multiobjective optimization
Hand gesture recognition has emerged as an attractive and promising method in human-machine interaction. Applying the simple, efficient and inexpensive devices is necessary for this kind of the application. In this paper, we propose a novel hand gesture recognition by investigating the forearm muscles movement data processing sensed by an array of 8 Force Sensor Resistor (FSR). The acquired data is sent by the wireless device to the processing unit that is more convenient for users. The feature extraction scheme is proposed to get some useful information about the data. A graph of the FSR sensed signals are constructed by considering the extracted features from them. Weights of the graph edge are computed by finding the differences between correspondents pair of sensors. Multiobjective optimization is applied to find the optimum parameters and providing the best description of the sensors' relations in each class. The proposed approach will be evaluated by conducting experiments. 10 volunteered persons participated in gathering FMG data in different classes of the hand gestures. As the results show the proposed method has good performance to recognize hand gestures and has almost 93% accuracy in the overall.
Anvaripour, Mohammad and Saif, Mehrdad. (2018). Hand gesture recognition using force myography of the forearm activities and optimized features. Proceedings of the IEEE International Conference on Industrial Technology, 2018-February, 187-192.