A CNN-LSTM model for the effects of object temperature, object hardness, and grip strength on human sensation grasped by a prosthetic hand-Reference-Cited by-同舟云学术

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A CNN-LSTM model for the effects of object temperature, object hardness, and grip strength on human sensation grasped by a prosthetic hand

Published:2022-11-07 Issue:11 Volume:82 Page:17009-17031
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Bai Jibo,Li Baojiang^ORCID,Wang Haiyan,Guo Yutin

Funder

Development and industrialization of intelligent multi degree of freedom arm based on perceptual fusion and collaborative control

Research on flexible joint and adaptive control technology for new upper limb prosthesis

Research on robot intelligent grasping technology based on visual touch fusion in unstructured environment

Publisher

Springer Science and Business Media LLC

Subject

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

Link

https://link.springer.com/content/pdf/10.1007/s11042-022-14086-2.pdf

Reference34 articles.

1. Ayad HG, Kamel MS (2007) Cumulative voting consensus method for partitions with variable number of clusters. IEEE Trans Pattern Anal Mach Intell 30(1):160–173

2. Bai J, Li B, Wang H, Guo Y (2022) Tactile perception information recognition of prosthetic hand based on dnn-lstm. IEEE Trans Instrum Meas 71:1–10. https://doi.org/10.1109/TIM.2022.3189644

3. Baishya SS, Bäuml B (2016) Robust material classification with a tactile skin using deep learning. In: 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp 8–15

4. Buescher G, Meier M, Walck G, Haschke R, Ritter HJ (2015) Augmenting curved robot surfaces with soft tactile skin. In: 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp 1514–1519

5. Cao L, Kotagiri R, Sun F, Li H, Huang W, Aye ZMM (2016) Efficient spatio-temporal tactile object recognition with randomized tiling convolutional networks in a hierarchical fusion strategy. In: Proceedings of the AAAI conference on artificial intelligence, vol 30

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