@inproceedings{cecbc51890f64d72a1fd785096dcbe9f,
title = "CochlearMotion: Head Gesture Recognition Leveraging Ear Canal Deformation Sensing",
abstract = "Hands-free interfaces have become increasingly popular due to the growing demands for convenient interaction with mobile and wearable devices. Among all of hands-free interfaces, head gesture interaction has shown great potential in providing alternatives in various real-world scenarios, such as interfaces for people with disabilities and Virtual/Augmented Reality applications. However, existing head gesture recognition systems require either Line-Of-Sight or specialized/customized hardware. Additionally, some approaches could raise potential privacy concerns. In this work, we propose CochlearMotion, a novel in-ear wearable system that achieves head gesture recognition by utilizing off-the-shelf earbuds with a built-in microphone. Specifically, we leverage sonar-like techniques to sense the unique deformation of the ear canal, which closely correlated with each head motion for cross-user head gesture recognition. Our extensive experimental evaluation shows that our system can achieve over 95\% recognition accuracy for six typical head gestures and works well in various real-world environments and scenarios.",
keywords = "Internet-of-Things (IoT), head gesture, human computer interaction (HCI), mobile computing, wearable",
author = "Youngone Lee and Sheng Tan and Zi Wang",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE Computer Society. All rights reserved.; 58th Hawaii International Conference on System Sciences, HICSS 2025 ; Conference date: 07-01-2025 Through 10-01-2025",
year = "2025",
doi = "10.24251/hicss.2025.529",
language = "English (US)",
series = "Proceedings of the Annual Hawaii International Conference on System Sciences",
publisher = "IEEE Computer Society",
pages = "4398--4407",
editor = "Bui, \{Tung X.\}",
booktitle = "Proceedings of the 58th Hawaii International Conference on System Sciences, HICSS 2025",
}