Direction-agnostic gesture recognition system using commercial WiFi devices
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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en
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11
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Computer Communications, Volume 216, pp. 34-44
Abstract
In recent years, channel state information (CSI) has been used to recognize hand gestures for contactless human–computer interaction. However, most existing solutions require precision hardware or prior learning at the same angle both during training and for inference/training in order to achieve high recognition accuracy. This requirement is unrealistic for practical instrumentation, where the orientation of a subject relative to the RF receiver may be arbitrary. We present direction-agnostic hand gesture recognition utilizing commercial WiFi devices to overcome low accuracy in non-trained observation angles. To achieve equal conditions in all recognition angles, first of all, through the circular antenna arrangement to mitigate the impact of user direction changes. Then, the orientation of users is estimated by the Fresnel zone model. Finally, the feature mapping model of users in different orientations is established, and the gesture features in the estimated direction are mapped to the benchmark direction to eliminate the influence caused by the change of user orientation. Experimental results in a typical indoor environment show that WiNDR has superior performance, and the average recognition accuracy of five common gestures is 92.38%.Description
Funding Information: This work was supported by the National Natural Science Foundation of China under Grant 62071244, the Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant KYCX20_0717, and China Scholarship Council, China under Grant 202108320270. Publisher Copyright: © 2024 Elsevier B.V.
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Qin, Y, Sigg, S, Pan, S & Li, Z 2024, 'Direction-agnostic gesture recognition system using commercial WiFi devices', Computer Communications, vol. 216, pp. 34-44. https://doi.org/10.1016/j.comcom.2023.12.033