A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials.
Falls are the leading cause of injury-related morbidity and mortality among older adults. Over 90 % of hip and wrist fractures and 60 % of traumatic brain injuries in older adults are due to falls. Another serious consequence of falls among older adults is the 'long lie' experienced by individuals w...
| Published in: | Medical & Biological Engineering & Computing Vol. 55; no. 1; pp. 45 - 56 |
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| Main Authors: | , , , , , , |
| Format: | research Journal Article |
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Springer Nature
Jan2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=120629450&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 120629450 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2017 vid: 55 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 120629450 120629450 NLM27106749 120629450 10.1007/s11517-016-1504-y NLM27106749 120629450 ppf: 45 ppct: 11 formats: fmt: @attributes: type: P tig: atl: A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials. aug: au: Aziz, Omar Musngi, Magnus Park, Edward Mori, Greg Robinovitch, Stephen Park, Edward J Robinovitch, Stephen N affil: School of Mechatronic Systems Engineering , Simon Fraser University , Surrey Canada sug: subj: Signal Processing, Computer Assisted Accelerometry Detection Algorithms Accidental Falls Sensitivity and Specificity Activities of Daily Living Young Adult Human Adult Validation Studies Comparative Studies Evaluation Research Multicenter Studies Adult: 19-44 years ab: Falls are the leading cause of injury-related morbidity and mortality among older adults. Over 90 % of hip and wrist fractures and 60 % of traumatic brain injuries in older adults are due to falls. Another serious consequence of falls among older adults is the 'long lie' experienced by individuals who are unable to get up and remain on the ground for an extended period of time after a fall. Considerable research has been conducted over the past decade on the design of wearable sensor systems that can automatically detect falls and send an alert to care providers to reduce the frequency and severity of long lies. While most systems described to date incorporate threshold-based algorithms, machine learning algorithms may offer increased accuracy in detecting falls. In the current study, we compared the accuracy of these two approaches in detecting falls by conducting a comprehensive set of falling experiments with 10 young participants. Participants wore waist-mounted tri-axial accelerometers and simulated the most common causes of falls observed in older adults, along with near-falls and activities of daily living. The overall performance of five machine learning algorithms was greater than the performance of five threshold-based algorithms described in the literature, with support vector machines providing the highest combination of sensitivity and specificity. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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