An optimum accelerometer configuration and simple algorithm for accurately detecting falls

  • Authors:

  • A. K. Bourke;C. Ni Scanaill;K. M. Culhane;J. V. O'Brien;G. M. Lyons

  • Affiliations:

  • Biomedical Electronics Laboratory, Department of Electronic and Computer Eng., University of Limerick, Limerick, Ireland;Biomedical Electronics Laboratory, Department of Electronic and Computer Eng., University of Limerick, Limerick, Ireland;Biomedical Electronics Laboratory, Department of Electronic and Computer Eng., University of Limerick, Limerick, Ireland;Department of Physical Education and Sport Sciences, University of Limerick, Limerick, Ireland;Biomedical Electronics Laboratory, Department of Electronic and Computer Eng., University of Limerick, Limerick, Ireland

  • Venue:
  • BioMed'06 Proceedings of the 24th IASTED international conference on Biomedical engineering
  • Year:
  • 2006

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Abstract

This paper describes the development of an accurate, accelerometer based fall detection system capable of distinguish between Activities of Daily Living (ADL) and fall-events. Using simulated fall-events onto crash mats (under supervised conditions) and ADL performed by elderly subjects, distinguishing between falls and ADL is achieved using an accelerometer-based sensor, mounted on the trunk and thigh of the person. Data analysis was performed using MATLAB® to determine the peak accelerations recorded during eight different types of falls. A fall detection algorithm was proposed using simple thresholding techniques. Results from an evaluation of the detection algorithm show that a fall-event can be distinguished from an ADL with 100% accuracy using a single threshold applied to the resultant acceleration signal from a tri-axial accelerometer located at the chest. Thresholding was thus demonstrated to be capable of discriminating between an ADL and a fall-event, when those falls were simulated falls.