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Performance evaluation of classifiers on activity recognition for disasters mitigation using smartphone sensing

Activity recognition (ARs) is a classification problem that cuts across many domains. The introduction of ARs accuracy which may be significantly low with decision tree algorithm and the use of smartphone sensing in previous studies has proven its relevance for effective disaster mitigation in our s...

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Main Authors: Fatai Idowu, Sadiq, Selamat, Ali, Ibrahim, Roliana
Format: Article
Language:English
Published: Penerbit UTM Press 2015
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/58784/1/FataiIdowu2015_PerformanceEvaluationOfClassifiersOnActivityRecognition.pdf
http://eprints.utm.my/id/eprint/58784/
http://dx.doi.org/10.11113/jt.v77.6320
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spelling my.utm.587842021-12-16T07:22:47Z http://eprints.utm.my/id/eprint/58784/ Performance evaluation of classifiers on activity recognition for disasters mitigation using smartphone sensing Fatai Idowu, Sadiq Selamat, Ali Ibrahim, Roliana QA75 Electronic computers. Computer science Activity recognition (ARs) is a classification problem that cuts across many domains. The introduction of ARs accuracy which may be significantly low with decision tree algorithm and the use of smartphone sensing in previous studies has proven its relevance for effective disaster mitigation in our society. Smartphone sensing is an approach found to be useful for activity recognition to monitor people in large gatherings due to the power of embedded sensors on the handheld devices. In this paper, a multitask activity recognition architecture is proposed for proper monitoring of people in large gatherings to control disaster occurrences in crowd, flood, road and fire accidents using related activity scenario in time of danger. We implement the proposed architecture to determine the outcome of activity recognized with K-nearest neighbour (KNN) for k= 3 and 4 to compare performance to that of weka using accelerometer and digital compass (dc) sensors on the same dataset. The results of ARs accuracy of 100% and 99% in weka, 85% and 89% with KNN shows an improved performance in both tools. The performance of Multilayer Perceptron (MLP), Support Vector Machine (SVM), Naives baye (NB), Decision tree (DT), against KNN were investigated using precision, recall and f-measure in weka as well. The results show significant improvement with performance parameters on accelerometer and dc against the use of accelerometer sensor only with KNN and DT having low number of classified activity recognized on training and testing data. Penerbit UTM Press 2015 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/58784/1/FataiIdowu2015_PerformanceEvaluationOfClassifiersOnActivityRecognition.pdf Fatai Idowu, Sadiq and Selamat, Ali and Ibrahim, Roliana (2015) Performance evaluation of classifiers on activity recognition for disasters mitigation using smartphone sensing. Jurnal Teknologi, 77 (13). pp. 11-19. ISSN 0127-9696 http://dx.doi.org/10.11113/jt.v77.6320 DOI:10.11113/jt.v77.6320
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Fatai Idowu, Sadiq
Selamat, Ali
Ibrahim, Roliana
Performance evaluation of classifiers on activity recognition for disasters mitigation using smartphone sensing
description Activity recognition (ARs) is a classification problem that cuts across many domains. The introduction of ARs accuracy which may be significantly low with decision tree algorithm and the use of smartphone sensing in previous studies has proven its relevance for effective disaster mitigation in our society. Smartphone sensing is an approach found to be useful for activity recognition to monitor people in large gatherings due to the power of embedded sensors on the handheld devices. In this paper, a multitask activity recognition architecture is proposed for proper monitoring of people in large gatherings to control disaster occurrences in crowd, flood, road and fire accidents using related activity scenario in time of danger. We implement the proposed architecture to determine the outcome of activity recognized with K-nearest neighbour (KNN) for k= 3 and 4 to compare performance to that of weka using accelerometer and digital compass (dc) sensors on the same dataset. The results of ARs accuracy of 100% and 99% in weka, 85% and 89% with KNN shows an improved performance in both tools. The performance of Multilayer Perceptron (MLP), Support Vector Machine (SVM), Naives baye (NB), Decision tree (DT), against KNN were investigated using precision, recall and f-measure in weka as well. The results show significant improvement with performance parameters on accelerometer and dc against the use of accelerometer sensor only with KNN and DT having low number of classified activity recognized on training and testing data.
format Article
author Fatai Idowu, Sadiq
Selamat, Ali
Ibrahim, Roliana
author_facet Fatai Idowu, Sadiq
Selamat, Ali
Ibrahim, Roliana
author_sort Fatai Idowu, Sadiq
title Performance evaluation of classifiers on activity recognition for disasters mitigation using smartphone sensing
title_short Performance evaluation of classifiers on activity recognition for disasters mitigation using smartphone sensing
title_full Performance evaluation of classifiers on activity recognition for disasters mitigation using smartphone sensing
title_fullStr Performance evaluation of classifiers on activity recognition for disasters mitigation using smartphone sensing
title_full_unstemmed Performance evaluation of classifiers on activity recognition for disasters mitigation using smartphone sensing
title_sort performance evaluation of classifiers on activity recognition for disasters mitigation using smartphone sensing
publisher Penerbit UTM Press
publishDate 2015
url http://eprints.utm.my/id/eprint/58784/1/FataiIdowu2015_PerformanceEvaluationOfClassifiersOnActivityRecognition.pdf
http://eprints.utm.my/id/eprint/58784/
http://dx.doi.org/10.11113/jt.v77.6320
_version_ 1720436889558712320
score 13.211869

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