Paper Info
Title
Self-Powered Gesture Recognition with Ambient Light.
Abstract
We present a self-powered module for gesture recognition that utilizes small, low-cost photodiodes for both energy harvesting and gesture sensing. Operating in the photovoltaic mode, photodiodes harvest energy from ambient light. In the meantime, the instantaneously harvested power from individual photodiodes is monitored and exploited as clues for sensing finger gestures in proximity. Harvested power from all photodiodes are aggregated to drive the whole gesture-recognition module including the micro-controller running the recognition algorithm. We design robust, lightweight algorithm to recognize finger gestures in the presence of ambient light fluctuations. We fabricate two prototypes to facilitate user's interaction with smart glasses and smart watch. Results show 99.7%/98.3% overall precision/recall in recognizing five gestures on glasses and 99.2%/97.5% precision/recall in recognizing seven gestures on the watch. The system consumes 34.6 µW/74.3 µW for the glasses/watch and thus can be powered by the energy harvested from ambient light. We also test system's robustness under varying light intensities, light directions, and ambient light fluctuations, where the system maintains high recognition accuracy (> 96%) in all tested settings.
Year
DOI
Venue
2018
10.1145/3242587.3242635
UIST '18: The 31st Annual ACM Symposium on User Interface Software and Technology Berlin Germany October, 2018
Keywords
Field
DocType
Gesture recognition, visible light sensing, energy harvesting
Computer vision,Computer science,Gesture,Gesture recognition,Energy harvesting,Robustness (computer science),Human–computer interaction,Artificial intelligence,Recognition algorithm,Smartwatch,Photovoltaic system,Photodiode
Conference
ISBN
Citations 
PageRank 
978-1-4503-5948-1
1
0.35
References 
Authors
40
5
Name
Order
Citations
PageRank
Yichen Li125.79
Tianxing Li236017.94
Ruchir A. Patel310.35
Xing-Dong Yang49410.07
Xia Zhou5126470.59