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Step by Step: Early Detection of Diseases Using an Intelligent Floor

Scherf, Lisa and Kirchbuchner, Florian and von Wilmsdorff, Julian and Fu, Biying and Braun, Andreas and Kuijper, Arjan (2018):
Step by Step: Early Detection of Diseases Using an Intelligent Floor.
In: Ambient Intelligence, Cham, Springer, In: European Conference on Ambient Intelligence (AmI), Larnaca, Cyprus, 2018, In: Lecture Notes in Computer Science (LNCS), 11249, ISSN 0302-9743,
ISBN 978-3-030-03061-2,
DOI: 10.1007/978-3-030-03062-9_11,
[Online-Edition: https://doi.org/10.1007/978-3-030-03062-9_11],
[Conference or Workshop Item]

Abstract

The development of sensor technologies in smart homes helps to increase user comfort or to create safety through the recognition of emergency situations. For example, lighting in the home can be controlled or an emergency call can be triggered if sensors hidden in the floor detect a fall of a person. It makes sense to also use these technologies regarding prevention and early detection of diseases. By detecting deviations and behavioral changes through long-term monitoring of daily life activities it is possible to identify physical or cognitive diseases. In this work, we first examine in detail the existing possibilities to recognize the activities of daily life and the capability of such a system to conclude from the given data on illnesses. Then we propose a model for the use of floor-based sensor technology to help diagnose diseases and behavioral changes by analyzing the time spent in bed as well as the walking speed of users. Finally, we show that the system can be used in a real environment.

Item Type: Conference or Workshop Item
Erschienen: 2018
Creators: Scherf, Lisa and Kirchbuchner, Florian and von Wilmsdorff, Julian and Fu, Biying and Braun, Andreas and Kuijper, Arjan
Title: Step by Step: Early Detection of Diseases Using an Intelligent Floor
Language: English
Abstract:

The development of sensor technologies in smart homes helps to increase user comfort or to create safety through the recognition of emergency situations. For example, lighting in the home can be controlled or an emergency call can be triggered if sensors hidden in the floor detect a fall of a person. It makes sense to also use these technologies regarding prevention and early detection of diseases. By detecting deviations and behavioral changes through long-term monitoring of daily life activities it is possible to identify physical or cognitive diseases. In this work, we first examine in detail the existing possibilities to recognize the activities of daily life and the capability of such a system to conclude from the given data on illnesses. Then we propose a model for the use of floor-based sensor technology to help diagnose diseases and behavioral changes by analyzing the time spent in bed as well as the walking speed of users. Finally, we show that the system can be used in a real environment.

Title of Book: Ambient Intelligence
Series Name: Lecture Notes in Computer Science (LNCS)
Volume: 11249
Place of Publication: Cham
Publisher: Springer
ISBN: 978-3-030-03061-2
Uncontrolled Keywords: Mental health, Personal health, Home care, Smart home, Activity monitoring, Activities of daily living (ADLs)
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Title: European Conference on Ambient Intelligence (AmI)
Event Location: Larnaca, Cyprus
Event Dates: 2018
Date Deposited: 10 Jul 2019 08:29
DOI: 10.1007/978-3-030-03062-9_11
Official URL: https://doi.org/10.1007/978-3-030-03062-9_11
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