title: A neural network approach for determining gait modifications to reduce the contact force in knee joint implant.
abstract: There is a growing interest in non-surgical gait rehabilitation treatments to reduce the loading in the knee joint. In particular, synergetic kinematic changes required for joint offloading should be determined individually for each subject. Previous studies for gait rehabilitation designs are typically relied on a "trial-and-error" approach, using multi-body dynamic (MBD) analysis. However MBD is fairly time demanding which prevents it to be used iteratively for each subject. This study employed an artificial neural network to develop a cost-effective computational framework for designing gait rehabilitation patterns. A feed forward artificial neural network (FFANN) was trained based on a number of experimental gait trials obtained from literature. The trained network was then hired to calculate the appropriate kinematic waveforms (output) needed to achieve desired knee joint loading patterns (input). An auxiliary neural network was also developed to update the ground reaction force and moment profiles with respect to the predicted kinematic waveforms. The feasibility and efficiency of the predicted kinematic patterns were then evaluated through MBD analysis. Results showed that FFANN-based predicted kinematics could effectively decrease the total knee joint reaction forces. Peak values of the resultant knee joint forces, with respect to the bodyweight (BW), were reduced by 20% BW and 25% BW in the midstance and the terminal stance phases. Impulse values of the knee joint loading patterns were also decreased by 17% BW*s and 24%BW*s in the corresponding phases. The FFANN-based framework suggested a cost-effective forward solution which directly calculated the kinematic variations needed to implement a given desired knee joint loading pattern. It is therefore expected that this approach provides potential advantages and further insights into knee rehabilitation designs.
authors: State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, 710049 Xi'an, Shaanxi, China.
authors: Marzieh Mostafavizadeh
authors: MM
authors: Ardestani
authors: State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, 710049 Xi'an, Shaanxi, China.
authors: Zhenxian
authors: Z
authors: Chen
authors: State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, 710049 Xi'an, Shaanxi, China. Electronic address: menlwang@mail.xjtu.edu.cn.
authors: Ling
authors: L
authors: Wang
authors: State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, 710049 Xi'an, Shaanxi, China.
authors: Qin
authors: Q
authors: Lian
authors: State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, 710049 Xi'an, Shaanxi, China.
authors: Yaxiong
authors: Y
authors: Liu
authors: State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, 710049 Xi'an, Shaanxi, China.
authors: Jiankang
authors: J
authors: He
authors: State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, 710049 Xi'an, Shaanxi, China.
authors: Dichen
authors: D
authors: Li
authors: State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, 710049 Xi'an, Shaanxi, China; Institute of Medical and Biological Engineering, School of Mechanical Engineering, University of Leeds, Leeds LS2 9JT, UK.
authors: Zhongmin
authors: Z
authors: Jin
mesh_terms: D001696:Biomechanical Phenomena; D005260:Female; D005684:Gait; D006801:Humans; D007719:Knee Joint / Q000502:physiology*; D007720:Knee Prosthesis; D008297:Male; D055595:Mechanical Phenomena; D016571:Neural Networks, Computer; D012046:Rehabilitation / Q000379:methods*; D016474:Weight-Bearing
keywords: Gait modification; Kinematics; Knee joint loading; Multi-body dynamics; Neural network