An intelligent video surveillance system for fall and anesthesia detection for elderly and patients

Rajabi, Hamed; Nahvi, Manoochehr

doi:10.1109/pria.2015.7161644

Cited by 17 publications

(7 citation statements)

References 15 publications

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“…There are also a lot of works used box bounding an ellipse to detect fall. They surrounded the moving object into the video by using box [26]- [32] or ellipse [33]- [37]. They computed the height and width of the box [26], [27], [31], [32].…”

Section: Related Workmentioning

confidence: 99%

Fall Detection for Elderly People Using the Variation of Key Points of Human Skeleton

2019

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In the area of health care, fall is a dangerous problem for aged persons. Sometimes, they are a serious cause of death. In addition to that, the number of aged persons will increase in the future. Therefore, it is necessary to develop an accurate system to detect fall. In this paper, we present spatiotemporal method to detect fall form videos filmed by surveillance cameras. Firstly, we computed key points of human skeleton. We calculated distances and angles between key points of each two pair sequences frames. After that, we applied Principal Component Analysis (PCA) to unify the dimension of features. Finally, we utilized Support Vector Machine (SVM), Decision Tree, Random Forest and K Nearest Neighbors (KNN) to classify features. We found that SVM is the best classifier to our method. The results of our algorithm are as follow: accuracy is 98.5%, sensitivity is 97% and the specificity is 100%.INDEX TERMS Fall detection, health care, human pose estimation.

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Section: Related Workmentioning

confidence: 99%

Fall Detection for Elderly People Using the Variation of Key Points of Human Skeleton

2019

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“…An alternative option for background estimation and subtraction is the application of Gaussian mixture models (GMM), a technique used in [ 7 , 11 , 14 , 78 , 92 ], among others, that models the values associated with specific pixels as a mix of Gaussian distributions.…”

Section: Discussionmentioning

confidence: 99%

“…Some systems, like the one presented in [ 7 ], apply a filter to determine silhouette contours. In this particular case, a Sobel filter is used, which determines a two-dimensional gradient of every image pixel.…”

Section: Discussionmentioning

confidence: 99%

“…Other features also used for characterization used in [ 7 , 39 ] include Hu moments, a group of six image moments in variables to translation, scale, rotation, and reflection, plus a seventh one, which changes sign for image reflection. These moments, assigned to a silhouette, do not change as a result of the point of view alterations associated with body displacements.…”

Section: Discussionmentioning

confidence: 99%

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Comprehensive Review of Vision-Based Fall Detection Systems

Gutiérrez

Rodríguez²,

Martín

2021

Sensors

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Vision-based fall detection systems have experienced fast development over the last years. To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the main scientific databases regarding this area during the last five years has been made. After a selection process, detailed in the Materials and Methods Section, eighty-one systems were thoroughly reviewed. Their characterization and classification techniques were analyzed and categorized. Their performance data were also studied, and comparisons were made to determine which classifying methods best work in this field. The evolution of artificial vision technology, very positively influenced by the incorporation of artificial neural networks, has allowed fall characterization to become more resistant to noise resultant from illumination phenomena or occlusion. The classification has also taken advantage of these networks, and the field starts using robots to make these systems mobile. However, datasets used to train them lack real-world data, raising doubts about their performances facing real elderly falls. In addition, there is no evidence of strong connections between the elderly and the communities of researchers.

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“…Manuel Martinez proposed a bed aligned map method to recognize human activities in bed [5]. Many researches have proposed many feature extraction and classifiers to detect human fall automatically like HOG [8], fuzzy rules [9], SVM [7], GMM [6], DAG [10].…”

Section: Related Workmentioning

confidence: 99%

Surveillance Camera Based Fall Detection System Using Long Short Term Memoryfor Elderly People

BaghavathiPriya¹

2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

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Event detection in videos is becoming an emerging area of research now a day. Monitoring of people activities using a surveillance camera is an essential one in a recent lifestyle for safety and security. The surveillance cameras are used in a wide variety of places such as in public places, Hospitals, Schools, and Homes for the beneficiaries of common people, patients, children and the elderly. In case of any emergency or abnormal events, immediate notification should be given to the respective people. The abnormal events are recognized from the videos using deep architectures. The goal of event detection in videos is to detect simple and complex actions in real-time data. This has a lot of attention in real-time ambient assisted living environments especially for elder people who live alone in the home. In this paper, a deep architecture of long short term memory recurrent network is proposed to detect fall actions in video inputs..

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An intelligent video surveillance system for fall and anesthesia detection for elderly and patients

Cited by 17 publications

References 15 publications

Fall Detection for Elderly People Using the Variation of Key Points of Human Skeleton

Fall Detection for Elderly People Using the Variation of Key Points of Human Skeleton

Comprehensive Review of Vision-Based Fall Detection Systems

Surveillance Camera Based Fall Detection System Using Long Short Term Memoryfor Elderly People

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