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РН
SpaceX
Роскосмос
NASA
СпутникиРНSpaceXРоскосмосNASAThis paper presents a self-adaptive software for efficiently generating personalized lower-limb rehabilitation mechanisms, which based on patients’ parameters such as height, thigh length, shank length and gender. The proposed system utilizes clustering method and the GA-SVM classifier to predict the optimal standard gait trajectory for patients, combined with a GA-BFGS hybrid optimization algorithm to determine the geometric parameters of a 1-degree-of-freedom (DOF) six-bar mechanisms which can best similarize the target trajectory. The innovation of this paper is in its use of clustering method to map a vast amount of disordered user trajectories to a limited set of standardized trajectories, thereby enabling user-to-institution matching. Featuring an intuitive interface, the software integrates trajectory recommendation, mechanism synthesis and motion visualization into a unified workflow, thereby enabling the generation of patient-specific rehabilitation mechanisms for diverse anthropometric characteristics. Experimental validation demonstrates that the synthesized mechanisms achieve an average trajectory deviation of 8.2 mm, confirming the software’s clinical applicability in personalized rehabilitation.
This paper presents a self-adaptive software for efficiently generating personalized lower-limb rehabilitation mechanisms, which based on patients’ parameters such as height, thigh length, shank length and gender. The proposed system utilizes clustering method and the GA-SVM classifier to predict the optimal standard gait trajectory for patients, combined with a GA-BFGS hybrid optimization algorithm to determine the geometric parameters of a 1-degree-of-freedom (DOF) six-bar mechanisms which can best similarize the target trajectory. The innovation of this paper is in its use of clustering method to map a vast amount of disordered user trajectories to a limited set of standardized trajectories, thereby enabling user-to-institution matching. Featuring an intuitive interface, the software integrates trajectory recommendation, mechanism synthesis and motion visualization into a unified workflow, thereby enabling the generation of patient-specific rehabilitation mechanisms for diverse anthropometric characteristics. Experimental validation demonstrates that the synthesized mechanisms achieve an average trajectory deviation of 8.2 mm, confirming the software’s clinical applicability in personalized rehabilitation.
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Hefei University of Technology, Hefei, Anhui, 230000, China
Yue Cheng, Baiye Xin, Yating Zhang & Ping Zhao
Authors
Correspondence to Ping Zhao.
School of Mechanical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
Jianrong Tan
School of Mechanical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
Zhenyu Liu
Mechanical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
Weifei Hu
© 2027 The Chinese Mechanical Engineering Society
Cheng, Y., Xin, B., Zhang, Y., Zhao, P. (2027). Design Software of Gait Rehabilitation Mechanism for Users with Various Body Parameters. In: Tan, J., Liu, Z., Hu, W. (eds) Advances in Mechanical Design. ICMD 2025. Mechanisms and Machine Science, vol 206. Springer, Singapore. https://doi.org/10.1007/978-981-95-7904-4_63
Download citationDOI: https://doi.org/10.1007/978-981-95-7904-4_63
Published: 25 June 2026
Publisher Name: Springer, Singapore
Print ISBN: 978-981-95-7903-7
Online ISBN: 978-981-95-7904-4
eBook Packages: Mechanical Engineering (R0)Springer Nature Proceedings excluding Computer Science
| # | Наименование патента |
|---|---|
| 1 | ACCELEROMETER-BASED GAIT ANALYSIS |
| 2 | ACCELEROMETER-BASED GAIT ANALYSIS |