Evaluating the Efficiency of Several Machine Learning Algorithms for Fall Detection

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

Abstract

Elderly falls are a growing phenomenon observed within the world. Ac-cording to World Health Organization (WHO), it is the second leading cause of unintentional or accidental deaths among the elderly. Thus, the need for research regarding the development of fall detection systems is imperative. Researchers have utilized various approaches to develop fall detection systems, significant number of which have employed Machine Leaning (ML) algorithms for fall detection. In this study, we evaluated the efficiency of six ML algorithms on a public fall detection dataset. A robust deep neural network for fall detection (FD-DNN) is identified to be the cur-rent state-of-the-art, it detects falls by using a self-built sensor that consumes low power. By evaluating the efficiency of six machine learning algorithms on a publicly available joint fall detection dataset, the accuracy of the fall detection was increased from 99.17% to 99.88% by using the K-nearest Neighbor indicating that common machine learning algorithms can achieve identical or higher accuracy rendering the complex and expensive deep neural network-based fall detection systems inefficient.
Original languageEnglish
Title of host publicationIntelligent Human Computer Interaction - 13th International Conference, IHCI 2021, Revised Selected Papers
Subtitle of host publicationIntelligent Human Computer Interaction
EditorsJong-Hoon Kim, Javed Khan, Madhusudan Singh, Uma Shanker Tiwary, Marigankar Sur, Dhananjay Singh
Place of PublicationNetherlands
PublisherSpringer
Pages610-620
Number of pages11
ISBN (Electronic)9783030984045
ISBN (Print)9783030984038
DOIs
Publication statusPublished - 20 Mar 2022
EventIHCI2021: 13th International Conference on Intelligent Human Computer Interaction - Kent, Ohio, United States
Duration: 20 Dec 202122 Dec 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13184 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceIHCI2021: 13th International Conference on Intelligent Human Computer Interaction
Abbreviated titleIHCI2021
Country/TerritoryUnited States
CityOhio
Period20/12/2122/12/21

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