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Erschienen in: Health and Technology 4/2019

08.02.2019 | Original Paper

A novel fall detection algorithm for elderly using SHIMMER wearable sensors

verfasst von: Amir Mehmood, Adnan Nadeem, Muhammad Ashraf, Turki Alghamdi, Muhammad Shoaib Siddiqui

Erschienen in: Health and Technology | Ausgabe 4/2019

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Abstract

Fall is one of the major cause of deaths in elderly along with other chronic diseases in all over the world. Therefore, it is important to find a cost effective, non-intrusive and lightweight solution for early fall detection and prevention in elderly. Several fall detection systems have been proposed, using the different types of sensors and techniques. In this paper, a novel fall detection technique, using the wearable SHIMMER™ sensors, is proposed, which identifies the fall event, using Mahalanobis distance on real-time data. It is more robust than other conventional distance measure techniques, followed in existing fall detection systems. We first developed a real dataset that consists of three daily life activities, such as walking, sitting (on) and getting up (from) a chair, and standing still. These activities are the main cause of fall in elderlies. The proposed algorithm was tested and validated, to identify the fall event. It produced the promising results, which are comparable to the state-of-the-art fall detection techniques.
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Metadaten
Titel
A novel fall detection algorithm for elderly using SHIMMER wearable sensors
verfasst von
Amir Mehmood
Adnan Nadeem
Muhammad Ashraf
Turki Alghamdi
Muhammad Shoaib Siddiqui
Publikationsdatum
08.02.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Health and Technology / Ausgabe 4/2019
Print ISSN: 2190-7188
Elektronische ISSN: 2190-7196
DOI
https://doi.org/10.1007/s12553-019-00298-4

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