The study and the design of novel methodologies and techniques for user's activity and gesture recognition is of great interest and a hot topic in human-computer interactions. Hand gesture recognition techniques based on computer-vision have yielded impressive results, but they involve users' privacy concerns, therefore other sensing approaches are of interest. In this work, a novel machine learning methodology based on passive electromagnetic sensing that exploits commodity Wi-Fi signals is proposed. Such an approach has been preliminary validated in a real house environment with a classification accuracy of 98%.
Real-Time CSI-Based Wireless Gesture Recognition for Human-Machine Interaction / Polo, A.; Salucci, M.; Verzura, S.; Zhou, Z.; Massa, A.. - STAMPA. - (2021), pp. 1-4. (Intervento presentato al convegno MOCAST 2021 tenutosi a Thessaloniki, Greece nel 5th-7th July 2021) [10.1109/MOCAST52088.2021.9493383].
Real-Time CSI-Based Wireless Gesture Recognition for Human-Machine Interaction
Polo A.;Salucci M.;Massa A.
2021-01-01
Abstract
The study and the design of novel methodologies and techniques for user's activity and gesture recognition is of great interest and a hot topic in human-computer interactions. Hand gesture recognition techniques based on computer-vision have yielded impressive results, but they involve users' privacy concerns, therefore other sensing approaches are of interest. In this work, a novel machine learning methodology based on passive electromagnetic sensing that exploits commodity Wi-Fi signals is proposed. Such an approach has been preliminary validated in a real house environment with a classification accuracy of 98%.File | Dimensione | Formato | |
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