Real-time elderly activity monitoring system based on a tri-axial accelerometer.
Purpose. The purpose of this study is to develop an automatic human movement classification system for the elderly using single waist-mounted tri-axial accelerometer. Methods. Real-time movement classification algorithm was developed using a hierarchical binary tree, which can classify activities of...
| Publicado en: | Disability & Rehabilitation: Assistive Technology Vol. 5; no. 4; pp. 247 - 254 |
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| Autores principales: | , , , , , |
| Formato: | algorithm equations & formulas pictorial research tables/charts tracings Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jul2010
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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=105026748&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105026748 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17483107 1X04 jtl: Disability & Rehabilitation: Assistive Technology issn: 17483107 maglogo: Y pubinfo: dt: Jul2010 vid: 5 iid: 4 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 105026748 2010683195 10.3109/17483101003718112 NLM20302417 105026748 ppf: 247 ppct: 7 formats: fmt: @attributes: type: P tig: atl: Real-time elderly activity monitoring system based on a tri-axial accelerometer. aug: au: Kang DW Choi JS Lee JW Chung SC Park SJ Tack GR affil: Department of Biomedical Engineering, Konkuk University, Chungju, Korea sug: subj: Accelerometry Monitoring, Physiologic In Old Age Accidental Falls Activities of Daily Living Adult Aged Algorithms Data Analysis Software Descriptive Statistics Funding Source Human Movement Classification Movement Evaluation Running Technology Trends Walking Wireless Communications Adult: 19-44 years Aged: 65+ years ab: Purpose. The purpose of this study is to develop an automatic human movement classification system for the elderly using single waist-mounted tri-axial accelerometer. Methods. Real-time movement classification algorithm was developed using a hierarchical binary tree, which can classify activities of daily living into four general states: (1) resting state such as sitting, lying, and standing; (2) locomotion state such as walking and running; (3) emergency state such as fall and (4) transition state such as sit to stand, stand to sit, stand to lie, lie to stand, sit to lie, and lie to sit. To evaluate the proposed algorithm, experiments were performed on five healthy young subjects with several activities, such as falls, walking, running, etc. Results. The results of experiment showed that successful detection rate of the system for all activities were about 96%. To evaluate long-term monitoring, 3 h experiment in home environment was performed on one healthy subject and 98% of the movement was successfully classified. Conclusions. The results of experiment showed a possible use of this system which can monitor and classify the activities of daily living. For further improvement of the system, it is necessary to include more detailed classification algorithm to distinguish several daily activities. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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