Journal ArticleDOI
Automatic Monocular System for Human Fall Detection Based on Variations in Silhouette Area
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TLDR
This paper proposes to use variations in silhouette area that are obtained from only one camera to find the silhouette, and shows that the proposed feature is view invariant.Abstract:
Population of old generation is growing in most countries. Many of these seniors are living alone at home. Falling is among the most dangerous events that often happen and may need immediate medical care. Automatic fall detection systems could help old people and patients to live independently. Vision-based systems have advantage over wearable devices. These visual systems extract some features from video sequences and classify fall and normal activities. These features usually depend on camera's view direction. Using several cameras to solve this problem increases the complexity of the final system. In this paper, we propose to use variations in silhouette area that are obtained from only one camera. We use a simple background separation method to find the silhouette. We show that the proposed feature is view invariant. Extracted feature is fed into a support vector machine for classification. Simulation of the proposed method using a publicly available dataset shows promising results.read more
Citations
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Journal ArticleDOI
A Survey on Activity Detection and Classification Using Wearable Sensors
TL;DR: This is one of the first surveys to provide such breadth of coverage across different wearable sensor systems for activity classification, and found that these single sensing modalities laid the foundation for hybrid works that tackle a mix of global and local interaction-type activities.
Journal ArticleDOI
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.
Journal ArticleDOI
Deep Learning for Fall Detection: Three-Dimensional CNN Combined With LSTM on Video Kinematic Data
TL;DR: A three-dimensional convolutional neural network (3-D CNN) based method for fall detection is developed, which only uses video kinematic data to train an automatic feature extractor and could circumvent the requirement for large fall dataset of deep learning solution.
Journal ArticleDOI
Fall detection devices and their use with older adults: a systematic review.
TL;DR: In general, older adults appear to be interested in using fall-detection devices although they express concerns over privacy and understanding exactly what the device is doing at specific times.
Journal ArticleDOI
A Smart Phone-Based Pocket Fall Accident Detection, Positioning, and Rescue System
Lih-Jen Kau,Chih-Sheng Chen +1 more
TL;DR: A distinguished fall accident detection accuracy up to 92% on the sensitivity and 99.75%" on the specificity can be obtained when a set of 450 test actions in nine different kinds of activities are estimated by using the proposed cascaded classifier, which justifies the superiority of the proposed algorithm.
References
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Proceedings ArticleDOI
Background subtraction techniques: a review
TL;DR: A review of the main methods and an original categorisation based on speed, memory requirements and accuracy can effectively guide the designer to select the most suitable method for a given application in a principled way.
Journal ArticleDOI
A survey on visual surveillance of object motion and behaviors
TL;DR: This paper reviews recent developments and general strategies of the processing framework of visual surveillance in dynamic scenes, and analyzes possible research directions, e.g., occlusion handling, a combination of two and three-dimensional tracking, and fusion of information from multiple sensors, and remote surveillance.
Proceedings ArticleDOI
Fall detection - Principles and Methods
Norbert Noury,Anthony Fleury,Pierre Rumeau,Alan K. Bourke,Gearóid Ó Laighin,Vincent Rialle,Jean-Eric Lundy +6 more
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.
Journal ArticleDOI
SVM-Based Multimodal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms, and First Experimental Results
TL;DR: The aim is to quantify criteria such as the international activities of daily living (ADL) or the French Autonomie Gerontologie Groupes Iso-Ressources (AGGIR) scales, by automatically classifying the different ADL performed by the subject during the day.
Journal ArticleDOI
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.