Natural hand gesture recognition with an electronic textile goniometer

Gesture recognition allows distinguishing specific user motions that intend to express a message. The recognized gestures can be used in various applications such as human–computer interface (HCI), clinical practice including rehabilitation, and personal identification. We propose a method of recognizing upper-limb motion gestures for HCI using electronic textile sensors, which consist of a double-layered structure with complementary resistance characteristics. For gesture recognition, we apply dynamic time warping (DTW) as it exhibits a high performance with simple computations for dynamic signals. We verified the functional feasibility of the proposed method from the data of 10 subjects performing 6 HCI gestures. The gesture classification accuracy for all subjects was 85.4%, although each subject separately achieved a higher performance. In fact, six subjects achieved a perfect recognition performance (100% recognition accuracy); three subjects achieved an accuracy of 98.6%, and one achieved an accuracy of 97.2%.