Simulation of MobiFall Dataset for Fall Detection Using MATLAB Classifier Algorithms

Rashid, FA; Sandrasegaran, K; Kong, X

Simulation of MobiFall Dataset for Fall Detection Using MATLAB Classifier Algorithms

Rashid, FA Sandrasegaran, K

Kong, X

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Developments in eSystems Engineering, DeSE, 2022, 2021-December, pp. 520-525
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Rashid, FA
dc.contributor.author	Sandrasegaran, K https://orcid.org/0000-0003-1270-5756
dc.contributor.author	Kong, X https://orcid.org/0000-0001-7855-858X
dc.date	2021-12-07
dc.date.accessioned	2023-04-10T23:04:37Z
dc.date.available	2023-04-10T23:04:37Z
dc.date.issued	2022-01-01
dc.identifier.citation	Proceedings - International Conference on Developments in eSystems Engineering, DeSE, 2022, 2021-December, pp. 520-525
dc.identifier.isbn	9781665408882
dc.identifier.issn	2161-1343
dc.identifier.uri	http://hdl.handle.net/10453/169410
dc.description.abstract	Fall accidents are considered one of the significant global public health concerns, and the largest proportion of fatal accidents are experienced by elderly people. Currently, there is a demand for creating an effective machine learning-based fall detection system that is significantly portable at a low cost. Public datasets are available for simulating an effective classifier for development. Hence, the current study is aimed at simulating the MobiFall fall detection dataset to acquire an effective machine learning classifier. The methodology included a study of the various fall detection systems as well as general features for machine learning classifications. The most suitable potential combination of machine learning algorithms that will provide the best accuracy, precision, sensitivity, specificity and lowest training time was developed via simulation models using MATLAB. Input data was selected from the MobiFall dataset for simulations. Up to 23 algorithms, including Decision Trees, Discriminant Analysis, Naïve Bayes Classifiers, Support Vector Machines, KNN and available Ensemble Classifiers, were simulated. Four sets of experiments were done using accelerometers with varying features and cross-validation. Currently, a combination of Quadratic SVM, Cubic SVM and Fine KNN was chosen to be used as the most appropriate classifier to train a fall detection system.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings - International Conference on Developments in eSystems Engineering, DeSE
dc.relation.ispartof	International Conference on Developments in eSystems Engineering
dc.relation.isbasedon	10.1109/DESE54285.2021.9719380
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Simulation of MobiFall Dataset for Fall Detection Using MATLAB Classifier Algorithms
dc.type	Conference Proceeding
utslib.citation.volume	2021-December
utslib.location.activity	Sharjah, United Arab Emirates
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-04-10T23:04:36Z
pubs.finish-date	2021-12-10
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2021-12-07
pubs.volume	2021-December
dc.location	Piscataway, USA

Abstract:

Fall accidents are considered one of the significant global public health concerns, and the largest proportion of fatal accidents are experienced by elderly people. Currently, there is a demand for creating an effective machine learning-based fall detection system that is significantly portable at a low cost. Public datasets are available for simulating an effective classifier for development. Hence, the current study is aimed at simulating the MobiFall fall detection dataset to acquire an effective machine learning classifier. The methodology included a study of the various fall detection systems as well as general features for machine learning classifications. The most suitable potential combination of machine learning algorithms that will provide the best accuracy, precision, sensitivity, specificity and lowest training time was developed via simulation models using MATLAB. Input data was selected from the MobiFall dataset for simulations. Up to 23 algorithms, including Decision Trees, Discriminant Analysis, Naïve Bayes Classifiers, Support Vector Machines, KNN and available Ensemble Classifiers, were simulated. Four sets of experiments were done using accelerometers with varying features and cross-validation. Currently, a combination of Quadratic SVM, Cubic SVM and Fine KNN was chosen to be used as the most appropriate classifier to train a fall detection system.

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http://hdl.handle.net/10453/169410