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An Internet of Things Based Bed-Egress Alerting Paradigm Using Wearable Sensors in Elderly Care Environment.
Awais, Muhammad; Raza, Mohsin; Ali, Kamran; Ali, Zulfiqar; Irfan, Muhammad; Chughtai, Omer; Khan, Imran; Kim, Sunghwan; Ur Rehman, Masood.
Afiliação
  • Awais M; Faculty of Medicine and Health, School of Psychology, University of Leeds, Leeds LS2 9JT, UK. m.awais@leeds.ac.uk.
  • Raza M; Design Engineering and Mathematics Department, Middlesex University, London NW4 4BT, UK. m.raza@mdx.ac.uk.
  • Ali K; Design Engineering and Mathematics Department, Middlesex University, London NW4 4BT, UK. k.ali@mdx.ac.uk.
  • Ali Z; School of Computing, Ulster University, Newtownabbey BT37 0QB, UK. z.ali@ulster.ac.uk.
  • Irfan M; Electrical Engineering Department, Najran University, Najran 61441, Saudi Arabia. miditta@nu.edu.sa.
  • Chughtai O; Department of Electrical and Computer Engineering, COMSATS University Wah Campus, Punjab 47050, Pakistan. omer.chughtai@outlook.com.
  • Khan I; Department of Electrical Engineering, University of Engineering & Technology, Peshawar 25000, Pakistan. imran_khan@uetpeshawar.edu.pk.
  • Kim S; School of Electrical Engineering, University of Ulsan, Ulsan 680-749, Korea. sungkim@ulsan.ac.kr.
  • Ur Rehman M; School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK. masood.urrehman@glasgow.ac.uk.
Sensors (Basel) ; 19(11)2019 May 31.
Article em En | MEDLINE | ID: mdl-31159252
ABSTRACT
The lack of healthcare staff and increasing proportions of elderly population is alarming. The traditional means to look after elderly has resulted in 255,000 reported falls (only within UK). This not only resulted in extensive aftercare needs and surgeries (summing up to £4.4 billion) but also in added suffering and increased mortality. In such circumstances, the technology can greatly assist by offering automated solutions for the problem at hand. The proposed work offers an Internet of things (IoT) based patient bed-exit monitoring system in clinical settings, capable of generating a timely response to alert the healthcare workers and elderly by analyzing the wireless data streams, acquired through wearable sensors. This work analyzes two different datasets obtained from divergent families of sensing technologies, i.e., smartphone-based accelerometer and radio frequency identification (RFID) based accelerometer. The findings of the proposed system show good efficacy in monitoring the bed-exit and discriminate other ambulating activities. Furthermore, the proposed work manages to keep the average end-to-end system delay (i.e., communications of sensed data to Data Sink (DS)/Control Center (CC) + machine-based feature extraction and class identification + feedback communications to a relevant healthcare worker/elderly) below 1 10 th of a second.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Screening_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Screening_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article