A Survey on Recent Advances in Wearable Fall Detection Systems.
With advances in medicine and healthcare systems, the average life expectancy of human beings has increased to more than 80 yrs. As a result, the demographic old-age dependency ratio (people aged 65 or above relative to those aged 15–64) is expected to increase, by 2060, from ∼28% to ∼50% in the Eur...
| Publicado en: | BioMed Research International pp. 1 - 19 |
|---|---|
| Autores principales: | , |
| Formato: | review tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
1/13/2020
|
| 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=141194575&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141194575 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 1/13/2020 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 141194575 141194575 141194575 10.1155/2020/2167160 141194575 ppf: 1 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Survey on Recent Advances in Wearable Fall Detection Systems. aug: au: Ramachandran, Anita Karuppiah, Anupama affil: Department of Computer Science & Information Systems, BITS, Pilani, Bangalore, India sug: subj: Accidental Falls Prevention and Control Assistive Technology Devices Utilization Wearable Sensors Literature Review ab: With advances in medicine and healthcare systems, the average life expectancy of human beings has increased to more than 80 yrs. As a result, the demographic old-age dependency ratio (people aged 65 or above relative to those aged 15–64) is expected to increase, by 2060, from ∼28% to ∼50% in the European Union and from ∼33% to ∼45% in Asia (Ageing Report European Economy, 2015). Therefore, the percentage of people who need additional care is also expected to increase. For instance, per studies conducted by the National Program for Health Care of the Elderly (NPHCE), elderly population in India will increase to 12% of the national population by 2025 with 8%–10% requiring utmost care. Geriatric healthcare has gained a lot of prominence in recent years, with specific focus on fall detection systems (FDSs) because of their impact on public lives. According to a World Health Organization report, the frequency of falls increases with increase in age and frailty. Older people living in nursing homes fall more often than those living in the community and 40% of them experience recurrent falls (World Health Organization, 2007). Machine learning (ML) has found its application in geriatric healthcare systems, especially in FDSs. In this paper, we examine the requirements of a typical FDS. Then we present a survey of the recent work in the area of fall detection systems, with focus on the application of machine learning. We also analyze the challenges in FDS systems based on the literature survey. pubtype: Academic Journal doctype: review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|