Research on gesture recognition of smart data fusion features in the IoT-Reference-Cited by-同舟云学术

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Research on gesture recognition of smart data fusion features in the IoT

Published:2019-01-25 Issue:22 Volume:32 Page:16917-16929
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Tan Chong,Sun Ying,Li Gongfa,Jiang Guozhang,Chen Disi,Liu Honghai

Funder

National Natural Science Foundation of China

National Defense Pre-Research Foundation of Wuhan University of Science and Technology

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/article/10.1007/s00521-019-04023-0/fulltext.html

Reference53 articles.

1. Chen D, Li G, Sun Y, Kong J, Jiang G, Tang H, Ju Z, Yu H, Liu H (2017) An interactive image segmentation method in hand gesture recognition. Sensors 17:253. https://doi.org/10.3390/s17020253

2. Sun Y, Li C, Li G, Jiang G, Jiang D, Liu H, Zheng Z, Shu W (2018) Gesture recognition based on kinect and sEMG signal fusion. Mobile Netw Appl 23(4):797–805

3. Li G, Zhang L, Sun Y, Kong J (2018) Towards the sEMG hand: internet of things sensors and haptic feedback application. Multimed Tools Appl. https://doi.org/10.1007/s11042-018-6293-x

4. He Y, Li G, Sun Y, Zhao Y, Jiang G (2018) Numerical simulation-based optimization of contact stress distribution and lubrication conditions in the straight worm drive. Strength Mater 50(11):157. https://doi.org/10.1007/s11223-018-9955-z

5. Jadooki S, Mohamad D, Saba T, Almazyad A, Rehman A (2017) Fused features mining for depth-based hand gesture recognition to classify blind human communication. Neural Comput Appl 28:3285. https://doi.org/10.1007/s00521-016-2244-5

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