Detecting abnormal patterns of daily activities for the elderly living alone

Tingzhi Zhao, Hongbo Ni, Xingshe Zhou, Lin Qiang, Daqing Zhang, Zhiwen Yu

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

17 Scopus citations

Abstract

In order to reduce the potential risks associated with physically and cognitively impaired ability of the elderly living alone, in this work, we develop an automated method that is able to detect abnormal patterns of the elderly's entering and exiting behaviors collected from simple sensors equipped in home-based setting. With spatiotemporal data left by the elderly when they carrying out daily activities, a Markov Chains Model (MCM) based method is proposed to classify abnormal sequences via analyzing the probability distribution of the spatiotemporal activity data. The experimental evaluation conducted on a 128-day activity data of an elderly user shows a high detection ratio of 92.80% for individual activity and of 92.539% for the sequence consisting of a series of activities.

Original languageEnglish
Title of host publicationHealth Information Science - Third International Conference, HIS 2014, Proceedings
PublisherSpringer Verlag
Pages95-108
Number of pages14
ISBN (Print)9783319062686
DOIs
StatePublished - 2014
Event3rd International Conference on Health Information Science, HIS 2014 - Shenzhen, China
Duration: 22 Apr 201423 Apr 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8423 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on Health Information Science, HIS 2014
Country/TerritoryChina
CityShenzhen
Period22/04/1423/04/14

Keywords

  • Abnormal Pattern
  • Infrared Tube
  • MCM
  • Spatiotemporal

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