A study of hand gesture recognition with wireless channel modeling by using wearable devices

We applied a wearable wireless node to model the wireless channel for hand gesture recognition in this study. With transmission and reception of wireless signals, the received signal strength indications (RSSI) are obtained to model the wireless channel. When people wear a wearable device, their hand gestures make the devices to receive different RSSI patterns, through the respective channels. Thus, the channel is modelled to perform the hand gestures recognition. In this paper, we proposed a weighting method to investigate the recognition on two gestures of up and horizontal movement. Experimental results show that the recognition rate can reach highly to 99%.