EgoHand: Ego-centric Hand Pose Estimation and Gesture Recognition with Head-mounted Millimeter-wave Radar and IMUs

• URL: http://arxiv.org/abs/2501.13805v1
• Date: Thu, 23 Jan 2025 16:25:08 GMT
• Title: EgoHand: Ego-centric Hand Pose Estimation and Gesture Recognition with Head-mounted Millimeter-wave Radar and IMUs
• Authors: Yizhe Lv, Tingting Zhang, Yunpeng Song, Han Ding, Jinsong Han, Fei Wang,
• Abstract summary: Bottom-facing VR cameras can pose a risk of exposing sensitive information, such as private body parts or personal surroundings. We introduce EgoHand, a system that integrates millimeter-wave radar and IMUs for hand gesture recognition. In experiments, EgoHand can detect hand gestures with 90.8% accuracy.
• Score: 15.644891766887255
• License:
• Abstract: Recent advanced Virtual Reality (VR) headsets, such as the Apple Vision Pro, employ bottom-facing cameras to detect hand gestures and inputs, which offers users significant convenience in VR interactions. However, these bottom-facing cameras can sometimes be inconvenient and pose a risk of unintentionally exposing sensitive information, such as private body parts or personal surroundings. To mitigate these issues, we introduce EgoHand. This system provides an alternative solution by integrating millimeter-wave radar and IMUs for hand gesture recognition, thereby offering users an additional option for gesture interaction that enhances privacy protection. To accurately recognize hand gestures, we devise a two-stage skeleton-based gesture recognition scheme. In the first stage, a novel end-to-end Transformer architecture is employed to estimate the coordinates of hand joints. Subsequently, these estimated joint coordinates are utilized for gesture recognition. Extensive experiments involving 10 subjects show that EgoHand can detect hand gestures with 90.8% accuracy. Furthermore, EgoHand demonstrates robust performance across a variety of cross-domain tests, including different users, dominant hands, body postures, and scenes.

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