A Comparison of Four Neural Networks Algorithms on Locomotion Intention Recognition of Lower Limb Exoskeleton Based on Multi-source Information
Author:
Funder
Shanghai Science and Technology innovation action plan
Publisher
Springer Science and Business Media LLC
Subject
Bioengineering,Biophysics,Biotechnology
Link
https://link.springer.com/content/pdf/10.1007/s42235-023-00435-w.pdf
Reference47 articles.
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2. Yang, J. T., Sun, T. R., Cheng, L., & Hou, Z. G. (2022). Spatial repetitive impedance learning control for robot-assisted rehabilitation. IEEE/ASME Transactions on Mechatronics, 28, 1280–1290. https://doi.org/10.1109/TMECH.2022.3221931
3. Mokhtari, M., Taghizadeh, M., & Mazare, M. (2021). Impedance control based on optimal adaptive high order super twisting sliding mode for a 7-DOF lower limb exoskeleton. Meccanica, 56, 535–548. https://doi.org/10.1007/s11012-021-01308-4
4. Zhong, B. X., Da Silva, R. L., Li, M., Huang, H., & Lobaton, E. (2020). Environmental context prediction for lower limb prostheses with uncertainty quantification. IEEE Transactions on Automation Science and Engineering, 18, 458–470. https://doi.org/10.1109/TASE.2020.2993399
5. Tucker, M. R., Olivier, J., Pagel, A., Bleuler, H., Bouri, M., Lambercy, O., Millán, J. D. R., Riener, R., Vallery, H., & Gassert, R. (2015). Control strategies for active lower extremity prosthetics and orthotics: a review. Journal of Neuroengineering and Rehabilitation, 12, 1–30. https://doi.org/10.1186/1743-0003-12-1
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