Index-to-palm interaction plays a crucial role in Mixed Reality(MR) interactions. However, achieving a satisfactory inter-hand interaction experience is challenging with existing vision-based hand tracking technologies, especially in scenarios where only a single camera is available. Therefore, we introduce Palmpad, a novel sensing method utilizing a single RGB camera to detect the touch of an index finger on the opposite palm. Our exploration reveals that the incorporation of optical flow techniques to extract motion information between consecutive frames for the index finger and palm leads to a significant improvement in touch status determination. By doing so, our CNN model achieves 97.0% recognition accuracy and a 96.1% F1 score. In usability evaluation, we compare Palmpad with Quest's inherent hand gesture algorithms. Palmpad not only delivers superior accuracy 95.3% but also reduces operational demands and significantly improves users’ willingness and confidence. Palmpad aims to enhance accurate touch detection for lightweight MR devices.
https://dl.acm.org/doi/10.1145/3706598.3714130
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