Fall detection from a manual wheelchair: preliminary findings based on accelerometers using machine learning techniques.
Automated fall detection devices for individuals who use wheelchairs to minimize the consequences of falls are lacking. This study aimed to develop and train a fall detection algorithm to differentiate falls from wheelchair mobility activities using machine learning techniques. Thirty, healthy, ambu...
| Publicado en: | Assistive Technology Vol. 35; no. 6; pp. 523 - 532 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173272646&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173272646 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10400435 YVP jtl: Assistive Technology issn: 10400435 maglogo: Y pubinfo: dt: 2023 vid: 35 iid: 6 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 173272646 161718815 173272646 173272646 10.1080/10400435.2023.2177775 173272646 ppf: 523 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Fall detection from a manual wheelchair: preliminary findings based on accelerometers using machine learning techniques. aug: au: Abou, Libak Fliflet, Alexander Presti, Peter Sosnoff, Jacob J. Mahajan, Harshal P. Frechette, Mikaela L. Rice, Laura A. affil: Department of Physical Medicine & Rehabilitation, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA sug: subj: Accidental Falls Algorithms Wheelchairs Adverse Effects Machine Learning Physical Mobility Activities of Daily Living Human Simulations Neural Networks (Computer) Classification Accelerometers Wearable Sensors Pilot Studies Automation Cross Sectional Studies Image Processing, Computer Assisted Descriptive Statistics Sensitivity and Specificity Female Male Adult ROC Curve Funding Source Adult: 19-44 years Female Male ab: Automated fall detection devices for individuals who use wheelchairs to minimize the consequences of falls are lacking. This study aimed to develop and train a fall detection algorithm to differentiate falls from wheelchair mobility activities using machine learning techniques. Thirty, healthy, ambulatory, young adults simulated falls from a wheelchair and performed other wheelchair-related mobility activities in a laboratory. Neural Network classifiers were used to train the algorithm developed based on data retrieved from accelerometers mounted at the participant's wrist, chest, and head. Results indicate excellent accuracy to differentiate between falls and wheelchair mobility activities. The sensors mounted at the wrist, chest, and head presented with an accuracy of 100%, 96.9%, and 94.8%, respectively, using data from 258 falls and 220 wheelchair mobility activities. This pilot study indicates that a fall detection algorithm developed in a laboratory setting based on fall accelerometer patterns can accurately differentiate wheelchair-related falls and wheelchair mobility activities. This algorithm should be integrated into a wrist-worn devices and tested among individuals who use a wheelchair in the community. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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