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- W3202659011 abstract "Although hand gesture recognition has been widely explored with sensing modalities like IMU, electromyography and camera, it is still a challenge of those modalities to provide a compact, power-efficient on-board inferencing solution. In this work, we present a capacitive-sensing wristband surrounded by four single-end electrodes for on-board hand gesture recognition. By deploying a single convolutional hidden layer as the classifier at the sensing edge, the wristband can recognize seven hand gestures from a single user with an accuracy of 96.4%." @default.
- W3202659011 created "2021-10-11" @default.
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- W3202659011 date "2021-09-21" @default.
- W3202659011 modified "2023-10-12" @default.
- W3202659011 title "Capacitive Sensing Based On-board Hand Gesture Recognition with TinyML" @default.
- W3202659011 cites W2115466366 @default.
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- W3202659011 doi "https://doi.org/10.1145/3460418.3479287" @default.
- W3202659011 hasPublicationYear "2021" @default.
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