Map modeling for full body gesture using flex sensor and machine learning algorithms-Reference-Cited by-同舟云学术

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Map modeling for full body gesture using flex sensor and machine learning algorithms

Published:2022-06-13 Issue:6 Volume:28 Page:2319-2334
ISSN:0942-4962
Container-title:Multimedia Systems
language:en
Short-container-title:Multimedia Systems

Author:

Hekmat Arash,Zuping Zhang,Al-deen Hashim Saleh Sharaf

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Information Systems,Software

Link

https://link.springer.com/content/pdf/10.1007/s00530-022-00946-2.pdf

Reference54 articles.

1. Głomb, P., Romaszewski, M., Opozda, S., Sochan, A.: Choosing and modeling the Hand Gesture Database for a natural user interface. In: Gesture Workshop (2011)

2. Supancic, J.S., Rogez, G., Yang, Y., Shotton J., Ramanan, D.: Depth-based hand pose estimation: data, methods, and challenges. In: 2015 IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1868–1876. https://doi.org/10.1109/ICCV.2015.217

3. Tan, D.J., et al.: Fits like a glove: rapid and reliable hand shape personalization. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 5610-5619, https://doi.org/10.1109/CVPR.2016.605

4. Chen, X., Wang, G., Guo, H., Zhang, C., Wang, H., Zhang, L.: Mfa-net: Motion feature augmented network for dynamic hand gesture recognition from skeletal data. Sensors 19(2), 239 (2019)

5. Syed Mubarak Ali, S.A.A., Ahmad, N.S., Goh, P.: Flex sensor compensator via Hammerstein–Wiener modeling approach for improved dynamic goniometry and constrained control of a bionic hand. Sensors 19(18), 3896 (2019)

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