NDLI: On the data analysis for classification of elementary upper limb movements

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Biomedical Engineering Letters : Volume 4, Issue 3, September 2014

Biomedical Engineering Letters : Volume 4, Issue 2, June 2014

Biomedical Engineering Letters : Volume 4, Issue 1, March 2014

Biomedical Engineering Letters : Volume 3

Biomedical Engineering Letters : Volume 2

Biomedical Engineering Letters : Volume 1

On the data analysis for classification of elementary upper limb movements

Content Provider	Springer Nature Link
Author	Biswas, Dwaipayan Cranny, Andy Rahim, Ahmed F. Gupta, Nayaab Maharatna, Koushik Harris, Nick R. Ortmann, Steffen
Copyright Year	2014
Abstract	Body worn inertial sensors could be used to assess rehabilitation of patients with impaired upper limb motor control by detecting and classifying how many times particular arm movements (exercises) are made during normal activities. We present a systematic exploration to determine such a system.Kinematic data was collected from 18 healthy subjects using tri-axial inertial sensors (accelerometers and gyroscopes) located at two positions on the dominant arm as four fundamental arm movements were repeated 20 times each. Ten time domain features were extracted from individual and combinations of sensor axes data, and were used to train a classifier. Three different classifiers were investigated: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and support vector machine (SVM). Each was verified using a leave-one-subject-out technique for a generalized classification model, and a ten-fold cross validation technique for a personalized classification model.LDA repeatedly gave the better results when using features extracted from individual sensor axes data. When a personalized learning model is used with LDA, only a single tri-axial sensor (accelerometer or gyroscope) is required to classify all four of the upper limb movements with a sensitivity in the range 92–100%, using as few as 6-10 time-domain features. By comparison, the generalized model using LDA exhibited lower sensitivity and generally required more features (12–18), reflecting the greater variability inherent in a training set comprised of more than one individual’s data.We demonstrate that body worn inertial sensors can classify elementary arm movements using a low complexity algorithm.
Starting Page	403
Ending Page	413
Page Count	11
File Format	PDF
ISSN	20939868
Journal	Biomedical Engineering Letters
Volume Number	4
Issue Number	4
e-ISSN	2093985X
Language	English
Publisher	The Korean Society of Medical and Biological Engineering
Publisher Date	2015-01-15
Publisher Place	Korea
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Accelerometer Activity recognition Gyroscope movement classification Remote health monitoring Wireless body area network (WBAN) Biomedical Engineering Biophysics and Biological Physics Biomedicine general Medical and Radiation Physics
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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