In this paper, we explore using the back of hands for sensing hand gestures, which interferes less than glove-based approaches and provides better recognition than sensing at wrists and forearms. Our prototype, BackHand, uses an array of strain gauge sensors affixed to the back of hands, and applies machine learning techniques to recognize a variety of hand gestures. We conducted a user study with 10 participants to better understand gesture recognition accuracy and the effects of sensing locations. Results showed that sensor reading patterns differ significantly across users, but are consistent for the same user. The leave-one-user-out accuracy is low at an average of 27.4%, but reaches 95.8% average accuracy for 16 popular hand gestures when personalized for each participant. The most promising location spans the 1/8~1/4 area between the metacarpophalangeal joints (MCP, the knuckles between the hand and fingers) and the head of ulna (tip of the wrist).
Jhe-Wei Lin, Chiuan Wang, Yi Yao Huang, Kuan-Ting Chou, Hsuan-Yu Chen, Wei-Luan Tseng, and Mike Y. Chen. 2015. BackHand: Sensing Hand Gestures via Back of the Hand. In Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology (UIST ’15). Association for Computing Machinery, New York, NY, USA, 557–564.