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EMG signal based human stress level classification using wavelet packet transform

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Main Authors: Karthikeyan, Palanisamy, Murugappan, M., Dr., Sazali, Yaacob, Prof. Dr.
Other Authors: karthi_20910@yahoo.com
Format: Article
Language:English
Published: Springer-Verlag 2014
Subjects:
EMG
KNN classifier
Stress
Stroop colour word test
Wavelet packet transform
Online Access:http://dspace.unimap.edu.my:80/dspace/handle/123456789/34560
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spelling my.unimap-345602014-05-21T02:43:55Z EMG signal based human stress level classification using wavelet packet transform Karthikeyan, Palanisamy Murugappan, M., Dr. Sazali, Yaacob, Prof. Dr. karthi_20910@yahoo.com murugappan@unimap.edu.my s.yaacob@unimap.edu.my EMG KNN classifier Stress Stroop colour word test Wavelet packet transform Link to publisher's homepage at http://link.springer.com/ Recent days, Electromyogram (EMG) signal acquired from muscles can be useful to measure the human stress levels. The aim of this present work to investigate the relationship between the changes in human stress levels to muscular tensions through Electromyography (EMG) in a stimulated stress-inducement environment. The stroop colour word test protocol is used to induce the stress and EMG signal is acquired from left trapezius muscle of 10 female subjects using three surface electrodes. The acquired signals were preprocessed through wavelet denoising method and statistical features were extracted using Wavelet Packet Transform (WPT). EMG signals are decomposed to four levels using db5 mother wavelet function. Frequency band information's of third and fourth levels are considered for descriptive analysis. Totally, seven statistical features were computed and analyzed to find the appropriate frequency band and feature for stress level assessment. A simple non-linear classifier (K Nearest Neighbor (KNN)) is used for classifying the stress levels. Statistical features derived from the frequency range of (0-31.5) Hz gives a maximum average classification accuracy of 90.70% on distinguishing the stress levels in minimum feature. 2014-05-21T02:43:55Z 2014-05-21T02:43:55Z 2012 Article Communications in Computer and Information Science, vol. 330(CCIS), 2012, pages 236-243 978-3-642-35197-6 (Online) 978-3-642-35196-9 (Print) 1865-0929 http://link.springer.com/chapter/10.1007%2F978-3-642-35197-6_26 http://dspace.unimap.edu.my:80/dspace/handle/123456789/34560 10.1007/978-3-642-35197-6_26 en Springer-Verlag
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic EMG
KNN classifier
Stress
Stroop colour word test
Wavelet packet transform
spellingShingle EMG
KNN classifier
Stress
Stroop colour word test
Wavelet packet transform
Karthikeyan, Palanisamy
Murugappan, M., Dr.
Sazali, Yaacob, Prof. Dr.
EMG signal based human stress level classification using wavelet packet transform
description Link to publisher's homepage at http://link.springer.com/
author2 karthi_20910@yahoo.com
author_facet karthi_20910@yahoo.com
Karthikeyan, Palanisamy
Murugappan, M., Dr.
Sazali, Yaacob, Prof. Dr.
format Article
author Karthikeyan, Palanisamy
Murugappan, M., Dr.
Sazali, Yaacob, Prof. Dr.
author_sort Karthikeyan, Palanisamy
title EMG signal based human stress level classification using wavelet packet transform
title_short EMG signal based human stress level classification using wavelet packet transform
title_full EMG signal based human stress level classification using wavelet packet transform
title_fullStr EMG signal based human stress level classification using wavelet packet transform
title_full_unstemmed EMG signal based human stress level classification using wavelet packet transform
title_sort emg signal based human stress level classification using wavelet packet transform
publisher Springer-Verlag
publishDate 2014
url http://dspace.unimap.edu.my:80/dspace/handle/123456789/34560
_version_ 1643797521266376704
score 13.214268

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