A preliminary study on deep neural network-based adaptive control for robotic ankle-foot prosthesis with speed and terrain flexibility
Document Type
Article
Publication Date
3-4-2026
Abstract
Powered prostheses assist lower-limb amputees by providing positive mechanical energy during ambulation. Optimal control parameters are crucial in providing proper support and smooth gait transition to the user. However, tuning remains a complex process. This research proposes a long short-term memory (LSTM)-based deep neural network (DNN) system to adaptively tune the virtual stiffness parameters of the robotic prosthesis across multiple walking speeds and terrains. It involves data collection from 19 healthy subjects performing walking, stairs ascent/descent and running. The DNN is then trained and its generalisability is tested with the presence of load. Overall, the trained model achieves a normalised root mean squared error (NRMSE) below 0.09, a correlation coefficient above 0.98 and a lag time below 100 µs, demonstrating high generalisation and strong adaptibility for prosthesis impedance control.
Keywords
Powered prosthesis, adaptive mechanism, deep neural network, environment uncertainty, ambulation speed
Publication Title
International Journal of Control
ISSN
0020-7179
DOI
10.1080/00207179.2025.2537253
Recommended Citation
Yeoh, Joslyn Ker Xin; Al Kouzbary, Mouaz; Abu Osman, Noor Azuan; Chan, Chow Khuen; Shasmin, Hanie Nadia; and Tham, Lai Kuan, "A preliminary study on deep neural network-based adaptive control for robotic ankle-foot prosthesis with speed and terrain flexibility" (2026). Research Publications (2026 to 2030). 32.
https://knova.um.edu.my/research_publications_2026_2030/32
Volume
99
Issue
3
First Page
783
Last Page
794
Publisher
Taylor & Francis