A cascade-classifier approach for fall detection

I. Putu Edy Suardiyana Putra, James Brusey, Elena Gaura

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

6 Citations (Scopus)
1 Downloads (Pure)

Abstract

The current machine learning algorithms in fall detection, especially those that use a sliding window, have a high com-putational cost because they need to compute the features from almost all samples. This computation causes energy drain and means that the associated wearable devices re-quire frequent recharging, making them less usable. This study proposes a cascade approach that reduces the compu-tational cost of the fall detection classifier. To examine this approach, accelerometer data from 48 subjects performing a combination of falls and ordinary behaviour is used to train 3 types of classifier (J48 Decision Tree, Logistic Regression, and Multilayer Perceptron). The results show that the cas-cade approach significantly reduces the computational cost both for learning the classifier and executing it once learnt. Furthermore, the Multilayer Perceptron achieves the high-est performance with precision of 93.5%, recall of 94.2%, and f-measure of 93.5%.

Original languageEnglish
Title of host publicationMOBIHEALTH 2015 - 5th EAI International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare through Innovations in Mobile and Wireless Technologies
EditorsAkram Alomainy, Yang Hao, William Whittow, Konstantina S. Nikita, Clive G. Parini
Place of PublicationBrussels
PublisherICST
Pages94-99
Number of pages6
ISBN (Electronic)9781631900884
DOIs
Publication statusPublished - 22 Dec 2015
Event5th EAI International Conference on Wireless Mobile Communication and Healthcare: Transforming Healthcare through Innovations in Mobile and Wireless Technologies - London, United Kingdom
Duration: 14 Oct 201516 Oct 2015
Conference number: 5
https://mobihealth.eai-conferences.org/2015/index.html

Publication series

NameMOBIHEALTH 2015 - 5th EAI International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare through Innovations in Mobile and Wireless Technologies

Conference

Conference5th EAI International Conference on Wireless Mobile Communication and Healthcare
Abbreviated titleMOBIHEALTH 2015
CountryUnited Kingdom
CityLondon
Period14/10/1516/10/15
Internet address

Bibliographical note

This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

Keywords

  • Cascade classifier
  • Com-putational cost
  • Fall detection
  • Machine Learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Health Informatics

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