Abstract
Today, cameras have become smaller and cheaper and can be utilized in various scenes. We took advantage of that to develop a thumb tip wearable device to estimate joint angles of a thumb as measuring human finger postures is important in terms of human-computer interface and to analyze human behavior. The device we developed consists of three small cameras attached at different angles so the cameras can capture the four fingers. We assumed that the appearance of the four fingers would change depending on the joint angles of the thumb. We made a convolutional neural network learn a regression relationship between the joint angles of the thumb and the images taken by the cameras. In this paper, we captured the keypoint positions of the thumb with a USB sensor device and calculated the joint angles to construct a dataset. The root mean squared error of the test data was 6.23\(^\circ \) and 4.75\(^\circ \).
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References
Chan, L., Chen, Y.L., Hsieh, C.H., Liang, R.H., Chen, B.Y.: CyclopsRing: enabling whole-hand and context-aware interactions through a fisheye ring. In: Proceedings of UIST, pp. 549–556 (2015)
Fukui, R., Watanabe, M., Shimosaka, M., Sato, T.: Hand shape classification with a wrist contour sensor. In: Proceedings of Experimental Robotics, pp. 939–949 (2013)
Kashiwagi, N., Sugiura, Y., Miyata, N., Tada, M., Sugimoto, M., Saito, H.: Measuring grasp posture using an embedded camera. In: Proceedings of WACVW, pp. 42–47 (2017)
Kim, D., et al.: Digits: freehand 3D interactions anywhere using a wrist-worn gloveless sensor. In: Proceedings of UIST, pp. 167–176 (2012)
Miyata, N., Honoki, T., Maeda, Y., Endo, Y., Tada, M., Sugiura, Y.: Wrap & sense: grasp capture by a band sensor. In: Proceedings of UIST, pp. 87–89 (2016)
Mueller, F., Mehta, D., Sotnychenko, O., Sridhar, S., Casas, D., Theobalt, C.: Real-time hand tracking under occlusion from an egocentric RGB-D sensor. In: Proceedings of ICCVW, pp. 1284–1293 (2017)
Rekimoto, J.: Gesturewrist and gesturepad: unobtrusive wearable interaction devices. In: Proceedings of ISWC, pp. 21–27 (2001)
Simon, T., Joo, H., Matthews, I., Sheikh, Y.: Hand keypoint detection in single images using multiview bootstrapping. In: Proceedings of CVPR, pp. 1145–1153 (2017)
Sridhar, S., Mueller, F., Oulasvirta, A., Theobalt, C.: Fast and robust hand tracking using detection-guided optimization. In: Proceedings of CVPR, pp. 3213–3221 (2015)
Vardy, A., Robinson, J., Cheng, L.T.: The WristCam as input device. In: Proceedings of ISWC (1999)
Zimmermann, C., Brox, T.: Learning to estimate 3D hand pose from single RGB images. In: Proceedings of ICCV, pp. 4903–4911 (2017)
Acknowledgements
This work was supported by JST AIP-PRISM Grant Number JPMJCR18Y2, Grant-in-Aid for JSPS Research Fellow Grant Number JP17J05489.
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Ienaga, N., Kawai, W., Fujita, K., Miyata, N., Sugiura, Y., Saito, H. (2019). A Thumb Tip Wearable Device Consisting of Multiple Cameras to Measure Thumb Posture. In: Carneiro, G., You, S. (eds) Computer Vision – ACCV 2018 Workshops. ACCV 2018. Lecture Notes in Computer Science(), vol 11367. Springer, Cham. https://doi.org/10.1007/978-3-030-21074-8_3
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DOI: https://doi.org/10.1007/978-3-030-21074-8_3
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