A Review of Abnormal Behavior Detection in Activities of Daily Living

Nian Chi Tay, Tee Connie, Thian Song Ong, Andrew Beng Jin Teoh, Pin Shen Teh

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new researchers to better understand the field of ABD in ADL and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend.

Original languageEnglish
Pages (from-to)5069-5088
Number of pages20
JournalIEEE Access
Volume11
DOIs
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Materials Science
  • General Engineering
  • Electrical and Electronic Engineering

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