Data set for fall events and daily activities from inertial sensors

Olukunle Ojetola, Elena Gaura, James Brusey

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

73 Citations (Scopus)


Wearable sensors are becoming popular for remote health monitoring as technology improves and cost reduces. One area in which wearable sensors are increasingly being used is falls monitoring. The elderly, in particular are vulnerable to falls and require continuous monitoring. Indeed, many attempts, with insufficient success have been made towards accurate, robust and generic falls and Activities of Daily Living (ADL) classification. A major challenge in developing solutions for fall detection is access to sufficiently large data sets.

This paper presents a description of the data set and the experimental protocols designed by the authors for the simu- lation of falls, near-falls and ADL. Forty-two volunteers were recruited to participate in an experiment that involved a set of scripted protocols. Four types of falls (forward, backward, lateral left and right) and several ADL were simulated. This data set is intended for the evaluation of fall detection al- gorithms by combining daily activities and transitions from one posture to another with falls. In our prior work, machine learning based fall detection algorithms were developed and evaluated. Results showed that our algorithm was able to discriminate between falls and ADL with an F-measure of 94%.

Original languageEnglish
Title of host publicationProceedings of the 6th ACM Multimedia Systems Conference (MMSys '15)
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Number of pages6
ISBN (Electronic)9781450333511
Publication statusPublished - 2015
Externally publishedYes
Event6th ACM Multimedia Systems Conference, MMSys 2015 - Portland, United States
Duration: 18 Mar 201520 Mar 2015


Other6th ACM Multimedia Systems Conference, MMSys 2015
Country/TerritoryUnited States


  • Wearable sensors
  • fall detection
  • protocols
  • annotated
  • health monitoring


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