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The design of a practical and reliable fall detector for community and institutional telecare

Doughty, K, Lewis, R, Mcintosh, A·2000·Journal of Telemedicine and Telecare

Abstract

Falls are one of the greatest obstacles to independent living for frail and elderly people. Their early detection is an important step in providing people with the reassurance and confidence necessary to maintain an active lifestyle. We have investigated a worn fall detector linked to a community alarm system. A worn device is the only one which is satisfactory, provided that it generates few false alarms. The fall detector we have developed is the size of a small radio pager. It uses a two-stage detection process which senses shock and the orientation of the wearer. A fall is detected within 20 s and triggers a radio signal to a community alarm system. Tests were devised using a jointed mannequin to simulate five modes of falling to understand the effects of impact at different parts of the body. This allowed us to select the appropriate trigger threshold and wearing positions for the sensor. Prototypes were evaluated with 20 people to observe false alarms. The final design allowed reliable detection in 180 different falling scenarios.

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Metadata

Authors
Doughty, K., Lewis, R., & Mcintosh, A.
Venue
Journal of Telemedicine and Telecare
Published
2000-02
Volume
6
Issue
1_suppl
Pages
150-154
ISSN
  • 1357-633X (ISSN-L)
  • 1758-1109
OpenAlex
W1976789043
Cited by
185
References
0
FWCI
1.32
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The design of a practical and reliable fall detector for community and institutional telecare. https://app.cochat.ai/papers/10.1258/1357633001934483
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Doughty, K., Lewis, R., & Mcintosh, A. (2000). The design of a practical and reliable fall detector for community and institutional telecare. Journal of Telemedicine and Telecare. https://doi.org/10.1258/1357633001934483

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.