Fall detection using supervised machine learning algorithms: A comparative study

Nabil Zerrouki, Fouzi Harrou, Amrane Houacine, Ying Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

35 Scopus citations


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.
Original languageEnglish (US)
Title of host publication2016 8th International Conference on Modelling, Identification and Control (ICMIC)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Print)9780956715777
StatePublished - Jan 5 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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