NDLI: Natural gait parameters prediction for gait rehabilitation via artificial neural network

Content Provider	IEEE Xplore Digital Library
Author	Lim, H.B. Trieu Phat Luu Hoon, K.H. Low, K.H.
Copyright Year	2010
Description	Author affiliation: School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore (Lim, H.B.; Trieu Phat Luu; Hoon, K.H.; Low, K.H.)
Abstract	Gait pattern planning is an important issue in robotic gait rehabilitation. Gait pattern is known to be related to gait parameters, such as cadence, stride length, and walking speed. Thus, prior before the discussion of gait pattern planning, the planning of gait parameters for natural walking should be addressed. This work utilizes multi-layer perceptron neural network (MLPNN) to predict natural gait parameters for a given subject. The inputs of the MLPNN are age, gender, body height, and body weight of the targeted subject. The MLPNN is trained to output a suitable walking speed and cadence for given subject. Two MLPNNs are trained to study the efficiency and accuracy in predicting the desired outputs, for two different setups. First setup is that the MLPNN is trained specifically for slow speed condition only. In second setup, the MLPNN is trained for both slow and normal speed conditions. The results of the MLPNNs are presented in this paper. The efficiency and accuracy of the MLPNNs are discussed.
Starting Page	5398
Ending Page	5403
File Size	1390735
Page Count	6
File Format	PDF
ISBN	9781424466740
ISSN	21530866
e-ISBN	9781424466764
DOI	10.1109/IROS.2010.5650311
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-18
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Legged locomotion Neurons Planning Artificial neural networks Humans Training
Content Type	Text
Resource Type	Article

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