Triaxial accelerometer-based fall and activities of daily life detection using machine learning

Turke Althobaiti, Stamos Katsigiannis, Naeem Ramzan

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
9 Downloads (Pure)

Abstract

The detection of activities of daily living (ADL) and the detection of falls is of utmost importance for addressing the issue of serious injuries and death as a consequence of elderly people falling. Wearable sensors can provide a viable solution for monitoring people in danger of falls with minimal external involvement from health or care home workers. In this work, we recorded accelerometer data from 35 healthy individuals performing various ADLs, as well as falls. Spatial and frequency domain features were extracted and used for the training of machine learning models with the aim of distinguishing between fall and no fall events, as well as between falls and other ADLs. Supervised classification experiments demonstrated the efficiency of the proposed approach, achieving an F1-score of 98.41% for distinguishing between fall and no fall events, and an F1-score of 88.11% for distinguishing between various ADLs, including falls.
Original languageEnglish
Article number3777
Number of pages19
JournalSensors
Volume20
Issue number13
DOIs
Publication statusPublished - 6 Jul 2020

Keywords

  • activities of daily living
  • fall detection
  • accelerometer
  • wearable sensors
  • machine learning

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