Fall detection using supervised machine learning algorithms: A comparative study

Handle URI:
http://hdl.handle.net/10754/622644
Title:
Fall detection using supervised machine learning algorithms: A comparative study
Authors:
Zerrouki, Nabil; Harrou, Fouzi; Houacine, Amrane; Sun, Ying ( 0000-0001-6703-4270 )
Abstract:
Fall incidents are considered as the leading cause of disability and even mortality among older adults. To address this problem, fall detection and prevention fields receive a lot of intention over the past years and attracted many researcher efforts. We present in the current study an overall performance comparison between fall detection systems using the most popular machine learning approaches which are: Naïve Bayes, K nearest neighbor, neural network, and support vector machine. The analysis of the classification power associated to these most widely utilized algorithms is conducted on two fall detection databases namely FDD and URFD. Since the performance of the classification algorithm is inherently dependent on the features, we extracted and used the same features for all classifiers. The classification evaluation is conducted using different state of the art statistical measures such as the overall accuracy, the F-measure coefficient, and the area under ROC curve (AUC) value.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Zerrouki N, Harrou F, Houacine A, Sun Y (2016) Fall detection using supervised machine learning algorithms: A comparative study. 2016 8th International Conference on Modelling, Identification and Control (ICMIC). Available: http://dx.doi.org/10.1109/ICMIC.2016.7804195.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2016 8th International Conference on Modelling, Identification and Control (ICMIC)
Issue Date:
5-Jan-2017
DOI:
10.1109/ICMIC.2016.7804195
Type:
Conference Paper
Additional Links:
http://ieeexplore.ieee.org/document/7804195/
Appears in Collections:
Conference Papers; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorZerrouki, Nabilen
dc.contributor.authorHarrou, Fouzien
dc.contributor.authorHouacine, Amraneen
dc.contributor.authorSun, Yingen
dc.date.accessioned2017-01-09T06:09:06Z-
dc.date.available2017-01-09T06:09:06Z-
dc.date.issued2017-01-05en
dc.identifier.citationZerrouki N, Harrou F, Houacine A, Sun Y (2016) Fall detection using supervised machine learning algorithms: A comparative study. 2016 8th International Conference on Modelling, Identification and Control (ICMIC). Available: http://dx.doi.org/10.1109/ICMIC.2016.7804195.en
dc.identifier.doi10.1109/ICMIC.2016.7804195en
dc.identifier.urihttp://hdl.handle.net/10754/622644-
dc.description.abstractFall incidents are considered as the leading cause of disability and even mortality among older adults. To address this problem, fall detection and prevention fields receive a lot of intention over the past years and attracted many researcher efforts. We present in the current study an overall performance comparison between fall detection systems using the most popular machine learning approaches which are: Naïve Bayes, K nearest neighbor, neural network, and support vector machine. The analysis of the classification power associated to these most widely utilized algorithms is conducted on two fall detection databases namely FDD and URFD. Since the performance of the classification algorithm is inherently dependent on the features, we extracted and used the same features for all classifiers. The classification evaluation is conducted using different state of the art statistical measures such as the overall accuracy, the F-measure coefficient, and the area under ROC curve (AUC) value.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/document/7804195/en
dc.rights(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.subjectCamerasen
dc.subjectClassification algorithmsen
dc.subjectFeature extractionen
dc.subjectMachine learning algorithmsen
dc.subjectSupport vector machinesen
dc.subjectTrainingen
dc.subjectTraining dataen
dc.titleFall detection using supervised machine learning algorithms: A comparative studyen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journal2016 8th International Conference on Modelling, Identification and Control (ICMIC)en
dc.eprint.versionPost-printen
dc.contributor.institutionUniversity of Sciences and Technology Houari Boumédienne, LCPTS, Faculty of Electronics and Computer Science, Algiers, Algeriaen
kaust.authorHarrou, Fouzien
kaust.authorSun, Yingen
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