Proceedings ArticleDOI
Automated Fall Detection From a Camera Using Support Vector Machine
TLDR
This paper proposes a camera based, novel, real-time automated fall detection framework for indoor environments, which has achieved detection rates compared to the state-of-art methods.Abstract:
Falls and fall-related fractures of a person is a major health problem and this issue is increasing day-by-day, especially for elderly who live alone. In this paper, we propose a camera based, novel, real-time automated fall detection framework for indoor environments. We process the frames on-the-fly for real time processing. We first apply background subtraction for detecting moving personin the indoor environment. We then extract relevant geometric features to classify fall from other daily activities of a person. Support vector machine (SVM) is applied to distinguish fall and other activities of a person. We have done experiments on publicly available dataset which is UR Fall Dataset. Our experiments demonstrate that our proposed framework has achieved detection rates compared to the state-of-art methods.read more
Citations
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Journal ArticleDOI
Comprehensive review of vision-based fall detection systems
TL;DR: A comprehensive revision of all published articles in the main scientific databases regarding this area during the last five years has been made to determine the course of its evolution and help new researchers.
Journal ArticleDOI
Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review.
Kathrin Seibert,Dominik Domhoff,Dominik Bruch,Matthias Schulte-Althoff,Daniel Fürstenau,Felix Biessmann,Karin Wolf-Ostermann +6 more
TL;DR: In this paper, the authors synthesize literature on application scenarios for AI in nursing care settings as well as highlight adjacent aspects in the ethical, legal, and social discourse surrounding the application of AI for nursing care.
Journal ArticleDOI
Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning
TL;DR: In this paper, different machine learning algorithms, such as random forest, K-nearest neighbours, support vector machine, long shortterm memory, bi-directional long short-term memory and convolutional neural networks, were used for data classification.
Book ChapterDOI
An Interpretable Machine Learning Model for Human Fall Detection Systems Using Hybrid Intelligent Models
Paulo Vitor de Campos Souza,Augusto Junio Guimarães,Vanessa Souza Araujo,Lucas Oliveira Batista,Thiago Silva Rezende +4 more
TL;DR: An assessment of falls and everyday situations in people by sensors dataset collected in fall simulation and the version of the hybrid model that acts on the most relevant dataset dimensions identifying falls obtained results that surpassed the other models submitted to the test.
Proceedings ArticleDOI
Accelerometer-based Human Fall Detection Using Fuzzy Entropy
TL;DR: A novel method based on Fuzzy Entropy measure is investigated to detect and distinguish human fall from other activities with a high degree of accuracy based on data acquired from an accelerometer device.
References
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Proceedings ArticleDOI
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TL;DR: The difficulty to compare the performances of the different systems due to the lack of a common framework is pointed out and a procedure for this evaluation is proposed.
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Robust Video Surveillance for Fall Detection Based on Human Shape Deformation
TL;DR: A new method is proposed to detect falls by analyzing human shape deformation during a video sequence, which gives very good results (as low as 0% error with a multi-camera setup) compared with other common image processing methods.
Journal ArticleDOI
Human fall detection on embedded platform using depth maps and wireless accelerometer
Bogdan Kwolek,Michal Kepski +1 more
TL;DR: This paper presents how to design and implement a low-cost system for reliable fall detection with very low false alarm ratio, a 365/7/24 embedded system permitting unobtrusive fall detection as well as preserving privacy of the user.
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Depth-based human fall detection via shape features and improved extreme learning machine
TL;DR: An automated fall detection approach that requires only a low-cost depth camera and a variable-length particle swarm optimization algorithm to optimize the number of hidden neurons, corresponding input weights, and biases of ELM is presented.