Genetic-algorithm-based feature-selection technique for fall detection using multi-placement wearable sensors

I. Putu Edy Suardiyana Putra*, Rein Vesilo

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

For a machine-learning-based fall-detection approach using wearable sensors, having a high number of features can not only cause a reduction in the detection rate because of irrelevant features, but it can also cause a high computational cost. Therefore, the number of features needs to be reduced through a feature-selection technique. However, current studies in fall-detection only consider features that can give an optimum detection rate without considering their computational cost. Having features with a high computational cost on wearable devices can cause their battery to drain fast. This paper presents a genetic-algorithm-based feature-selection technique that can search for a subset of low-computational-cost features from different sensor placements, where those features can give a relatively good detection rate in terms of F-score. The experimental results show that our technique is able to select a subset of low-computational-cost features that can achieve up to 97.7% of F-score on average. Compared to the SelectKBest and Recursive Feature Elimination (RFE) techniques (a filter and an embedded feature-selection technique, respectively), our approach is able to select features that can give a comparable F-score and significantly lower computational cost.

Original languageEnglish
Title of host publicationAdvances in body area networks I
Subtitle of host publicationpost-conference proceedings of BodyNets 2017
EditorsGiancarlo Fortino, Zhelong Wang
Place of PublicationCham
PublisherSpringer, Springer Nature
Pages319-332
Number of pages14
ISBN (Electronic)9783030028190
ISBN (Print)9783030028183
DOIs
Publication statusPublished - 2019
EventEuropean Alliance for Innovation (EAI) International Conference on Body Area Networks (12th : 2017) - Dalian, China
Duration: 28 Sep 201729 Sep 2017
Conference number: 12th

Publication series

NameInternet of Things
PublisherSpringer
ISSN (Print)2199-1073
ISSN (Electronic)2199-1081

Conference

ConferenceEuropean Alliance for Innovation (EAI) International Conference on Body Area Networks (12th : 2017)
Abbreviated titleBodyNets 2017
CountryChina
CityDalian
Period28/09/1729/09/17

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