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Sensors 2018, 18(2), 592; https://doi.org/10.3390/s18020592

On the Comparison of Wearable Sensor Data Fusion to a Single Sensor Machine Learning Technique in Fall Detection

Department of Electrical and Computer Engineering, University of Patras, 265 04 Patras, Greece
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Received: 9 January 2018 / Revised: 5 February 2018 / Accepted: 7 February 2018 / Published: 14 February 2018
(This article belongs to the Special Issue Soft Sensors and Intelligent Algorithms for Data Fusion)
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Abstract

In the context of the ageing global population, researchers and scientists have tried to find solutions to many challenges faced by older people. Falls, the leading cause of injury among elderly, are usually severe enough to require immediate medical attention; thus, their detection is of primary importance. To this effect, many fall detection systems that utilize wearable and ambient sensors have been proposed. In this study, we compare three newly proposed data fusion schemes that have been applied in human activity recognition and fall detection. Furthermore, these algorithms are compared to our recent work regarding fall detection in which only one type of sensor is used. The results show that fusion algorithms differ in their performance, whereas a machine learning strategy should be preferred. In conclusion, the methods presented and the comparison of their performance provide useful insights into the problem of fall detection. View Full-Text
Keywords: fall detection; data fusion; accelerometer; gyroscope; smartphone; wearable sensors; mHealth fall detection; data fusion; accelerometer; gyroscope; smartphone; wearable sensors; mHealth
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Tsinganos, P.; Skodras, A. On the Comparison of Wearable Sensor Data Fusion to a Single Sensor Machine Learning Technique in Fall Detection. Sensors 2018, 18, 592.

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