Design and Preliminary Testing of a Novel Variable-Damping Prosthetic Knee

Abstract

This article proposes a variable-damping prosthetic knee (ReKnee) with a novel hydraulic damper, and the intelligent control system is proposed. The LSTM network is proposed to identify the locomotion pattern under five road conditions. Predictive control is first applied to the hydraulic damping control for prosthetic knee. Prototype experiments are carried out to verify the performance of the control system. The experiment results show that LSTM possesses better accuracy and robustness than the traditional RNN network and other shallow machine learning algorithms, and NNPC damping control improves the gait symmetry compared to fuzzy logic control with decreasing in the range of 5.75–19.27% of the SI values when walking speed varies. It is demonstrated that the ReKnee can make a good performance on enhancing the accuracy of angle tracking and the approximation of normal gait characteristics.

KEYWORDS:

• Gait symmetry
• Locomotion pattern recognition
• Predictive damping control
• Prosthetic knee
• Transfemoral amputees
• Variable-damping

ACKNOWLEDGEMENTS

This work was supported by the National Natural Science Foundation of China [grant number 62073224]; National Key Research and Development Program of China [grant number 2018YFB1307303]; the program of China Scholarships Council [grant number 202108310200].

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number 62073224]; National Key Research and Development Program of China [grant number 2018YFB1307303; the program of China Scholarships Council [grant number 202108310200].

Notes on contributors

Xiaoming Wang

Xiaoming Wang received the BS degree in biomedical engineering from University of Shanghai for Science and Technology (USST), China, in 2018, where she is currently pursuing the PhD degree, taking a successive postgraduate and doctoral program. Her research interests include the development of intelligent perception and control of knee prosthesis, and bionic structure design of prosthesis. Email: [email protected]

Qiaoling Meng

Qiaoling Meng received the BS, MS, and PhD degrees in mechanical engineering from Shenyang University of Technology, China, Northeastern University, China, and University of Bologna, Italy, in 2002, 2008, and 2012, respectively. Since 2013, she was an Assistant Professor at USST, China, where she is currently an Associate Professor. Her current research interests include mechanical bionics, human biomechanics, robot dynamics. Email: [email protected]

Hongliu Yu

Hongliu Yu received the BS degree in electrical power engineering from Huazhong University of Science and Technology, China, in 1987, the MS degree in mechanical engineering from Zhengzhou University, China, in 1990, and the PhD degree in industrial engineering from University of Shanghai for Science and Technology, China, in 2009. From 1990 to 1994, he was an assistant professor at East China Jiaotong University, China. From 1994 to 2002, he was a senior mechanical engineer and manager at Guangdong Jianlibao FTB Packaging Ltd, China. Since 2002, he has been a professor and director with Rehabilitation Engineering and Technology Institute, Medical Instrument and Food Engineering Department, USST, China. He is the author of five books, more than 130 articles, and more than 100 patents. As the charging person, he founded the first education program of rehabilitation engineering and technology in China. His research interests include human bionic mechanics and intelligent control, rehabilitation robotics, man–machine intelligent interaction, and orthopedic devices and biomechanics.