Assessment of waist-worn tri-axial accelerometer based fall-detection algorithms using continuous unsupervised activities

Alan K. Bourke, Pepijn Van De Ven, Mary Gamble, Raymond O'Connor, Kieran Murphy, Elizabeth Bogan, Eamonn McQuade, Paul Finucane, Gearóid ÓLaighin, John Nelson

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

Abstract

This study aims to evaluate a variety of existing and novel fall detection algorithms, for a waist mounted accelerometer based system. Algorithms were tested against a comprehensive data-set recorded from 10 young healthy subjects performing 240 falls and 120 activities of daily living and 10 elderly healthy subjects performing 240 scripted and 52.4 hours of continuous unscripted normal activities. Results show that using a simple algorithm employing Velocity+Impact+Posture can achieve a low false-positive rate of less than 1 FP/day* (0.94FPs/day*) with a sensitivity of 94.6% and a specificity of 100%. The algorithms were tested using unsupervised continuous activities performed by elderly subjects living in the community, which is the target environment for a fall detection device.

Original languageEnglish
Title of host publication2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Pages2782-2785
Number of pages4
DOIs
Publication statusPublished - 2010
Event2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 - Buenos Aires, Argentina
Duration: 31 Aug 20104 Sep 2010

Publication series

Name2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10

Conference

Conference2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Country/TerritoryArgentina
CityBuenos Aires
Period31/08/104/09/10

Fingerprint

Dive into the research topics of 'Assessment of waist-worn tri-axial accelerometer based fall-detection algorithms using continuous unsupervised activities'. Together they form a unique fingerprint.

Cite this