Elderly Fall Detection Systems: A Literature Survey
Xueyi Wang,Joshua Ellul,George Azzopardi +2 more
- Vol. 7, pp 71-71
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TLDR
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.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.read more
Citations
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Journal ArticleDOI
A Study of Fall Detection in Assisted Living: Identifying and Improving the Optimal Machine Learning Method
Nirmalya Thakur,Chia Y. Han +1 more
TL;DR: A novel methodology based on the usage of k-folds cross-validation and the AdaBoost algorithm that improves the performance accuracy of the k-NN classifier-based fall detection system to the extent that it outperforms all similar works in this field.
Journal ArticleDOI
Towards an Accelerometer-Based Elderly Fall Detection System Using Cross-Disciplinary Time Series Features
Md. Jaber Al Nahian,Tapotosh Ghosh,Md. Hasan Al Banna,Mohammed Aseeri,Mohammed Nasir Uddin,Muhammad R. Ahmed,Mufti Mahmud,M. Shamim Kaiser +7 more
TL;DR: Wang et al. as mentioned in this paper proposed a novel pipeline for fall detection based on wearable accelerometer data and three publicly available datasets have been used to validate their proposed method, and more than 7700 cross-disciplinary time-series features were investigated for each of the datasets.
Journal ArticleDOI
Pre-Impact Fall Detection with CNN-Based Class Activation Mapping Method.
TL;DR: This paper reports an improvement on the prediction accuracy of pre-impact fall detection by applying a learning-based method on the real-time data from an IMU (inertial measurement unit)-sensor mounted on the waist, making it possible to achieve a high accuracy on a wearable device with the extracted features.
Journal ArticleDOI
Vision-based Human Fall Detection Systems using Deep Learning: A Review
TL;DR: In this paper , the authors discuss deep learning (DL)-based state-of-the-art non-intrusive (vision-based) fall detection techniques and present a survey on fall detection benchmark datasets.
Journal ArticleDOI
Country-Specific Interests towards Fall Detection from 2004–2021: An Open Access Dataset and Research Questions
Nirmalya Thakur,Chia Y. Han +1 more
TL;DR: In this paper, the authors present a study that uses the potential of the modern Internet of Everything lifestyle, where relevant Google Search data originating from different geographic regions can be interpreted to understand the underlining region-specific user interests towards a specific topic, which further demonstrates the public health need towards the same.
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