2020
1.
Wang, Xueyi; Ellul, Joshua; Azzopardi, George
Elderly fall detection systems: A literature survey Journal Article
Abstract | Links | BibTeX | Altmetric | Tags: convnets, deep learning, egocentric vision, fall detection, predictive analysis, wearables
@article{wang2020elderly,
title = {Elderly fall detection systems: A literature survey},
author = {Xueyi Wang and Joshua Ellul and George Azzopardi},
doi = {https://doi.org/10.3389/frobt.2020.00071},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Frontiers in Robotics and AI},
volume = {7},
pages = {71},
publisher = {Frontiers},
abstract = {Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial.},
keywords = {convnets, deep learning, egocentric vision, fall detection, predictive analysis, wearables},
pubstate = {published},
tppubtype = {article}
}
Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial.