Abstract
Virtual and Augmented reality (VR/AR) are widely deployed in industrial, medical, educational, and entertaining fields. The design of interactive interfaces has an impact on usability, comfort, and efficiency. Hand controllers and gestures are popularly used in VR/AR devices. However, users may suffer from overloading on the upper extremities while raising the hand or controller. Therefore, we released a microgesture library with 19 microgestures designed by ergonomists. Users can perform microgestures for an extended duration by resting the forearm on the tables to reduce the load on the upper extremity. Additionally, we collected a microgesture dataset of 2900 samples and utilized the C3D model to recognize the microgesture dataset. Finally, we achieved a recognition accuracy of 93.4% on the microgestures dataset.
Granted by the Key-Area Research and Development Program of Guangdong Province (No. 2019B010149001), the National Natural Science Foundation of China (No. 61960206007) and the 111 Project (B18005).
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Li, G., Liu, Y., Song, W., Wang, C., Wang, Y. (2021). A New Dataset and Recognition for Egocentric Microgesture Designed by Ergonomists. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12889. Springer, Cham. https://doi.org/10.1007/978-3-030-87358-5_7
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