A self-attention enhanced deep CNN-LSTM based irregular surface recognition approach for integration into lower limb prosthesis systems to ensure safety through predictive walking

A self-attention enhanced deep CNN-LSTM based irregular surface recognition approach for integration into lower limb prosthesis systems to ensure safety through predictive walking

Advancements in instrumentation and control systems for lower limb prostheses have substantially improved mobility for amputees. However, significant challenges persist when users encounter irregular surfaces, as most prosthetic systems lack the capability to dynamically adapt to surface variations....

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Bibliographic Details
Main Authors: Norazian, Subari, Kamarul Hawari, Ghazali, Ji, Yuanfa
Format: Article
Language:English
Published: IEEE 2025
Subjects:
TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/44911/
http://umpir.ump.edu.my/id/eprint/44911/1/A%20self-attention%20enhanced%20deep%20CNN-LSTM%20based%20irregular%20surface.pdf

Internet

http://umpir.ump.edu.my/id/eprint/44911/
http://umpir.ump.edu.my/id/eprint/44911/1/A%20self-attention%20enhanced%20deep%20CNN-LSTM%20based%20irregular%20surface.pdf

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