NDLI: Gait feature analysis of polyneuropathy patients

Content Provider	IEEE Xplore Digital Library
Author	Xingchen Wang Kuzmicheva, O. Spranger, M. Graser, A.
Copyright Year	2015
Description	Author affiliation: Inst. of Autom., Univ. of Bremen, Bremen, Germany (Xingchen Wang; Kuzmicheva, O.; Graser, A.) \|\| Neurological Rehabilitation Center, Bremen, Germany (Spranger, M.)
Abstract	Polyneuropathy (PNP) and aging both bring changes to the walking pattern of elderly people. However, the identification methods of PNP from gait patterns were not sufficiently investigated from a technical perspective. In this study an automated classification method was developed to discriminate the neuropathic gait from both young healthy and old healthy gait using artificial neural network (ANN). A robust markerless gait detection system was employed and experiments were conducted in normal clinical conditions on 10 young, 10 old and 10 neuropathy patients. Four types of gait features, namely temporal features, kinematic joint trajectories in time domain, the Fourier transform of joint angles in frequency domain, and the symmetry indexes, were extracted. One-way analysis of variance (ANOVA) was employed as a statistical analysis tool and feature selection method. Each type of features and the selected features obtained from ANOVA were served as the input of a two-layer-feed-forward neural network separately. A twofold cross validation method with enhanced generalization was utilized to evaluate the accuracy of classification. The ground truth information for the result validation was provided by the medical experts involved in the study. The outcome of individual feature set showed that the kinematic features in time domain reached the highest classification accuracies of 94.2%, 94.8% and 94.8% for three classes, while the symmetric features yielded the lowest. Combining two sets of features can improve the performance slightly and the best performance was achieved by using the selected significant features with accuracies of 96.2%, 97.0% and 96.9% respectively.
Starting Page	58
Ending Page	63
File Size	878563
Page Count	6
File Format	PDF
ISBN	9781479964772
DOI	10.1109/MeMeA.2015.7145172
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-07
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kinematics Artificial neural networks Legged locomotion Feature extraction Joints Accuracy Trajectory statistical analysis artificial neural network classification gait patterns polyneuropathy
Content Type	Text
Resource Type	Article

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