opisthenar: hand poses and finger tapping recognition

We introduce a vision-based technique to recognize static hand poses and dynamic finger tapping gestures. Our approach employs a camera on the wrist, with a view of the opisthenar (back of the hand) area. We envisage such cameras being included in a wrist-worn device such as a smartwatch, fitness tracker or wristband. Indeed, selected off-the-shelf smartwatches now incorporate a built-in camera on the side for photography purposes. However, in this configuration, the fingers are occluded from the view of the camera. The oblique angle and placement of the camera make typical vision-based techniques difficult to adopt. Our alternative approach observes small movements and changes in the shape, tendons, skin and bones on the opisthenar area. We train deep neural networks to recognize both hand poses and dynamic finger tapping gestures. While this is a challenging configuration for sensing, we tested the recognition with a real-time user test and achieved a high recognition rate of 89.4% (static poses) and 67.5% (dynamic gestures). Our results further demonstrate that our approach can generalize across sessions and to new users. Namely, users can remove and replace the wrist-worn device while new users can employ a previously trained system, to a certain degree. We conclude by demonstrating three applications and suggest future avenues of work based on sensing the back of the hand.

Hui-Shyong Yeo, Erwin Wu, Juyoung Lee, Aaron Quigley, and Hideki Koike. 2019. Opisthenar: Hand Poses and Finger Tapping Recognition by Observing Back of Hand Using Embedded Wrist Camera. In Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology (UIST ’19). Association for Computing Machinery, New York, NY, USA, 963–971. DOI:https://doi.org/10.1145/3332165.3347867