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  4. Inbed: A highly specialized system for bed-exit-detection and fall prevention on a geriatric ward
 
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Inbed: A highly specialized system for bed-exit-detection and fall prevention on a geriatric ward

Publikationstyp
Journal Article
Date Issued
2019-03-01
Sprache
English
Author(s)
Jähne-Raden, Nico  
Kulau, Ulf  
Marschollek, Michael  
Wolf, Klaus Hendrik  
TORE-URI
http://hdl.handle.net/11420/10752
Journal
Sensors  
Volume
19
Issue
5
Article Number
1017
Citation
Sensors (Switzerland) 19 (5): 1017 (2019-03-01)
Publisher DOI
10.3390/s19051017
Scopus ID
2-s2.0-85062409637
PubMed ID
30818871
Objective: In geriatric institutions, the risk of falling of patients is very high and frequently leads to fractures of the femoral neck, which can result in serious consequences and medical costs. With regard to the current numbers of elderly people, the need for smart solutions for the prevention of falls in clinical environments as well as in everyday life has been evolving. Methods: Hence, in this paper, we present the Inexpensive Node for bed-exit Detection (INBED), a comprehensive, favourable signaling system for bed-exit detection and fall prevention, to support the clinical efforts in terms of fall reduction. The tough requirements for such a system in clinical environments were gathered in close cooperation with geriatricians. Results: The conceptional efforts led to a multi-component system with a core wearable device, attached to the patients, to detect several types of movements such as rising, restlessness and—in the worst case—falling. Occurring events are forwarded to the nursing staff immediately by using a modular, self-organizing and dependable wireless infrastructure. Both, the hardware and software of the entire INBED system as well as the particular design process are discussed in detail. Moreover, a trail test of the system is presented. Conclusions: The INBED system can help to relieve the nursing staff significantly while the personal freedom of movement and the privacy of patients is increased compared to similar systems.
Subjects
Fall prevention
Geriatric
Health-enabling technology
Medical wearables
Wireless sensor network
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