Record number :
1732784
Title of article :
A neural network approach for determining gait modifications to reduce the contact force in knee joint implant
Author/Authors :
Ardestani، نويسنده , , Marzieh Mostafavizadeh and Chen، نويسنده , , Zhenxian and Wang، نويسنده , , Ling and Lian، نويسنده , , Qin and Liu، نويسنده , , Yaxiong and He، نويسنده , , Jiankang and Li، نويسنده , , Dichen and Jin، نويسنده , , Zhongmin، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2014
Pages :
13
From page :
1253
To page :
1265
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. tudy 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. 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.
Keywords :
Gait modification , Kinematics , Knee joint loading , neural network , Multi-body dynamics
Journal title :
Medical Engineering and Physics
Serial Year :
2014
Link To Document :
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