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iFace: Hand-Over-Face Gesture Recognition Leveraging Impedance Sensing

Mengxi Liu; Hymalai Bello; Bo Zhou; Paul Lukowicz; Jakob Karolus
In: Proceedings of the Augmented Humans International Conference 2024. Augmented Humans International Conference (AHs-2024), New York, NY, USA, AHs '24, ISBN 9798400709807, Association for Computing Machinery, 2024.


Hand-over-face gestures can provide important implicit interactions during conversations, such as frustration or excitement. However, in situations where interlocutors are not visible, such as phone calls or textual communication, the potential meaning contained in the hand-over-face gestures is lost. In this work, we present iFace, an unobtrusive, wearable impedance-sensing solution for recognizing different hand-over-face gestures. In contrast to most existing works, iFace does not require the placement of sensors on the user's face or hands. Instead, we proposed a novel sensing configuration, the shoulders, which remains invisible to both the user and outside observers. The system can monitor the shoulder-to-shoulder impedance variation caused by gestures through electrodes attached to each shoulder. We evaluated iFace in a user study with eight participants, collecting six kinds of hand-over-face gestures with different meanings. Using a convolutional neural network and a user-dependent classification, iFace reaches 82.58 % macro F1 score. We discuss potential application scenarios of iFace as an implicit interaction interface.


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