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Communication Dans Un Congrès Année : 2023

Hand gesture recognition using thin plate radiation and gated-recurrent-unit, based on ultrasound doppler

Résumé

In recent years, touchless technologies for human-computer interaction have been widely developed. Doppler sonar makes it possible to extract information from hand gestures by emitting/receiving ultrasounds, and gestures recognition is generally achieved using features extracted from a gesture sequence as input to Convolutional Neural Network. This work aims at achieving an accurate and rich acoustical touchless gesture recognition with a low number of transducers and a low complexity real-time classifier. For this purpose, we use a thin plate as an acoustic antenna, excited by a few piezoelectric actuators, and capture the echoes with microphones around the plate. High amplitude emissions on a large bandwidth are achievable with a better-integrated system. Signal features selected to contain meaningful information on rich 3D gestures are computed and used as an input to a small Gated-Recurrent-Unit neural network. We achieve the detection and classification of 11 3D gestures with an accuracy of 93.5% with our system.
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Dates et versions

cea-04543019 , version 1 (11-04-2024)

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Paul Glemain, Emmanuel Hardy, Charles Hudin, Pierre-Henri Orefice, Nazih Mechbal. Hand gesture recognition using thin plate radiation and gated-recurrent-unit, based on ultrasound doppler. IUS 2023 - 2023 IEEE 63st International Ultrasonics Symposium, Sep 2023, Montréal, Canada. ⟨10.1109/IUS51837.2023.10307268⟩. ⟨cea-04543019⟩
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