Head Gesture Recognition Based on Capacitive Sensors Using Deep Learning Algorithms

The current paper proposed and investigated the head motion recognition idea based on four capacitive sensors and deep learning models. The proposed system was designed to empower a tetraplegic person to control a remote device or an intelligent wheelchair. The capacitive sensors were placed around the neck using a necktie, which each volunteer who participated in this experiment was easy to use. The results show that the best-proposed deep learning model can determine each activity with a classification rate equal to 89.29% using capacitive raw data. During the experiments the deep learning models provided accuracy values in the range of 56.25% to 89.29%.