TY - GEN
T1 - Airfinger
T2 - 40th IEEE International Conference on Distributed Computing Systems, ICDCS 2020
AU - Zhang, Qian
AU - Cao, Yetong
AU - Chen, Huijie
AU - Li, Fan
AU - Yang, Song
AU - Wang, Yu
AU - Yang, Zheng
AU - Liu, Yunhao
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - Micro finger gesture recognition is an emerging approach to realize more friendly interaction between human and smart devices, especially for small wearable devices, such as smartwatches and virtual reality glasses. This paper proposes airFinger, a novel solution utilizing NIR light sensing to realize both real-time gesture recognition and finger tracking aiming at micro finger gestures. Using a custom NIR-based sensor with novel algorithms to capture subtle finger movements, airFinger enables to detect a rich set of micro finger gestures and track finger movements in terms of scrolling direction, velocity, and displacement. Besides, airFinger is capable of effective noise mitigation, gesture segmentation, and reducing false recognition due to the unintentional actions of users. Extensive experimental results demonstrate that airFinger has robustness against individual diversity, gesture inconsistency, and many other impacts. The overall performance reaches an average accuracy as high as 98.72% over a set of 8 micro finger gestures among 10,000 gesture samples collected from 10 volunteers.
AB - Micro finger gesture recognition is an emerging approach to realize more friendly interaction between human and smart devices, especially for small wearable devices, such as smartwatches and virtual reality glasses. This paper proposes airFinger, a novel solution utilizing NIR light sensing to realize both real-time gesture recognition and finger tracking aiming at micro finger gestures. Using a custom NIR-based sensor with novel algorithms to capture subtle finger movements, airFinger enables to detect a rich set of micro finger gestures and track finger movements in terms of scrolling direction, velocity, and displacement. Besides, airFinger is capable of effective noise mitigation, gesture segmentation, and reducing false recognition due to the unintentional actions of users. Extensive experimental results demonstrate that airFinger has robustness against individual diversity, gesture inconsistency, and many other impacts. The overall performance reaches an average accuracy as high as 98.72% over a set of 8 micro finger gestures among 10,000 gesture samples collected from 10 volunteers.
KW - Gesture recognition
KW - Interaction
KW - Light sensor
KW - Micro finger gesture
UR - http://www.scopus.com/inward/record.url?scp=85101995293&partnerID=8YFLogxK
U2 - 10.1109/ICDCS47774.2020.00073
DO - 10.1109/ICDCS47774.2020.00073
M3 - Conference contribution
AN - SCOPUS:85101995293
T3 - Proceedings - International Conference on Distributed Computing Systems
SP - 552
EP - 562
BT - Proceedings - 2020 IEEE 40th International Conference on Distributed Computing Systems, ICDCS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 November 2020 through 1 December 2020
ER -