2018
Tsinganos, Panagiotis; Skodras, Athanassios
On the Comparison of Wearable Sensor Data Fusion to a Single Sensor Machine Learning Technique in Fall Detection Journal Article
In: Sensors, vol. 18, no. 2, pp. 592, 2018, ISSN: 1424-8220.
Abstract | Links | BibTeX | Tags: accelerometer, data fusion, fall detection, gyroscope, mHealth, smartphone, wearable sensors
@article{Tsinganos2018b,
title = {On the Comparison of Wearable Sensor Data Fusion to a Single Sensor Machine Learning Technique in Fall Detection},
author = {Panagiotis Tsinganos and Athanassios Skodras},
url = {http://www.mdpi.com/1424-8220/18/2/592},
doi = {10.3390/s18020592},
issn = {1424-8220},
year = {2018},
date = {2018-02-01},
journal = {Sensors},
volume = {18},
number = {2},
pages = {592},
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.},
keywords = {accelerometer, data fusion, fall detection, gyroscope, mHealth, smartphone, wearable sensors},
pubstate = {published},
tppubtype = {article}
}
2017
Tsinganos, Panagiotis; Skodras, Athanassios
A smartphone-based fall detection system for the elderly Proceedings Article
In: Kovačič, Stanislav; Lončarić, Sven; Kristan, Matej; Štruc, Vitomir; Vučić, Mladen (Ed.): Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis, pp. 53–58, IEEE, Ljubljana, Slovenia, 2017, ISBN: 978-1-5090-4011-7.
Abstract | Links | BibTeX | Tags: accelerometer, ADLs, fall detection, falls, machine learning, smartphone
@inproceedings{Tsinganos2017a,
title = {A smartphone-based fall detection system for the elderly},
author = {Panagiotis Tsinganos and Athanassios Skodras},
editor = {Stanislav Kovačič and Sven Lončarić and Matej Kristan and Vitomir Štruc and Mladen Vučić},
url = {http://ieeexplore.ieee.org/document/8073568/},
doi = {10.1109/ISPA.2017.8073568},
isbn = {978-1-5090-4011-7},
year = {2017},
date = {2017-09-01},
booktitle = {Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis},
pages = {53--58},
publisher = {IEEE},
address = {Ljubljana, Slovenia},
abstract = {Falls can be severe enough to cause disabilities especially to frail populations. Thus, prompt health care provision is essential to prevent and restore any harm. The purpose of this study is to develop a smartphone-based fall detection system that can distinguish between falls and activities of daily living (ADL). The typical fall detection system consists of a sensing component and a notification module. Android devices, equipped with sensors and communication services, are the best candidates for the development of such systems. This work incorporates a threshold based algorithm, whose accuracy is enhanced by a k Nearest Neighbor (kNN) classifier. In addition, this paper proposes the implementation of a personalization and power regulation system. It achieves high fall detection accuracy, (97.53% sensitivity and 94.89% specificity), which is comparable to related works.},
keywords = {accelerometer, ADLs, fall detection, falls, machine learning, smartphone},
pubstate = {published},
tppubtype = {inproceedings}
}