Data set for fall events and daily activities from inertial sensors

O. Ojetola, E. Gaura, J. Brusey

    Research output: Chapter in Book/Report/Conference proceedingChapter

    72 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 2015
    ISBN (Print)978-145033351-1
    Publication statusPublished - Mar 2015
    EventMMSys 2015 - Portland, United States
    Duration: 18 Mar 201520 Mar 2015


    ConferenceMMSys 2015
    Country/TerritoryUnited States

    Bibliographical note

    This paper was given at the 6th ACM Multimedia Systems Conference, MMSys 2015; Portland; United States; 18 March 2015 through 20 March 2015. The paper is not available on the repository


    • Annotated
    • Fall detection
    • Health monitoring
    • Protocols
    • Wearable sensors


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