Detection and multi-class classification of falling in elderly people by deep belief network algorithms

Jahanjoo, Anice; Naderan, Marjan; Rashti, Mohammad Javad

doi:10.1007/s12652-020-01690-z

Cited by 25 publications

(13 citation statements)

References 50 publications

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“…Moreover, using a help button is worthless if the person has fainted or is immobilized. Such a framework for elderly fall classification and notification is proposed in [ 9 ]. Here, the tri-axial acceleration of human movement is measured with a cell phone.…”

Section: Related Workmentioning

confidence: 99%

“…Because of this lifestyle, people are suffering from many more diseases like chronic health disease, muscle weakness, labyrinthitis, osteoporosis. Besides, the number of lonely living beings is increasing with the advancement of technology which represents the necessity of a monitoring system to detect adverse events [ 9 ]. This model will be applicable in the medical alert system and even in the old home for monitoring individuals.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Indoor Human Fall Events Using Deep Learning

Sultana

Deb

Dhar

et al. 2021

Entropy

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Human fall identification can play a significant role in generating sensor based alarm systems, assisting physical therapists not only to reduce after fall effects but also to save human lives. Usually, elderly people suffer from various kinds of diseases and fall action is a very frequently occurring circumstance at this time for them. In this regard, this paper represents an architecture to classify fall events from others indoor natural activities of human beings. Video frame generator is applied to extract frame from video clips. Initially, a two dimensional convolutional neural network (2DCNN) model is proposed to extract features from video frames. Afterward, gated recurrent unit (GRU) network finds the temporal dependency of human movement. Binary cross-entropy loss function is calculated to update the attributes of the network like weights, learning rate to minimize the losses. Finally, sigmoid classifier is used for binary classification to detect human fall events. Experimental result shows that the proposed model obtains an accuracy of 99%, which outperforms other state-of-the-art models.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of Indoor Human Fall Events Using Deep Learning

Sultana

Deb

Dhar

et al. 2021

Entropy

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“…This was motivation for studying the abnormal activity detection. In the literature, researchers proposed different methods [1][2][3][4][5][6][7][8][11][12][13][14][15][16][17][18][19] to detect abnormal activity, researches focus on bellowing approaches. The abnormal activities detection techniques is briefly summarized in below table…”

Section: Introductionmentioning

confidence: 99%

Entropy Measurement to Extract the Signification of Abnormal Activity from Camera’s Frames and its Application for Fall Detection

2020

EJS

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Most of the indoor accidents are related with fall down. Many medical studies are point out that key factor for keeping patient’s life is fast response of monitoring system. In modern life, peoples are isolated with neighbor, especially in living quarters. Therefore many solutions are developed for falling down monitoring that base on wearable sensors. These methods require of an expensive sensors system with electric power supplier and telecommunication devices. In context of patients with disease and weak status, patients are trend to remove sensor system. This issue requires to find out another approach so that sensors system will not be needed. We study the fall detection by monitoring camera. For increase the accuracy, we proposed a simple and effective method to extract features of abnormal activities. By tracking the magnitude of entropy and its distribution, our fall detection model has a capability of differentiating falls from other activities

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“…A review of such traditional machine learning models is given in [8]. Notably, support vector machines, logistic regression, K-nearest neighbour, decision trees, and Multi-Layered Perceptron (MLP) [17] have been used in fall detection by several researchers.…”

Section: Traditional Methods For Fall Detectionmentioning

confidence: 99%

“…Falls happen to be the driving cause of deaths and hospitalizations for the elderly, and this has led to a significant cost to healthcare systems. The number of injuries caused by falls is expected to double by 2030 due to the aging population [17] if no preventive measures are undertaken [7]. This has led to a new area of research that concerns itself with the monitoring of activities of the elderly population.…”

Section: Overviewmentioning

confidence: 99%

Investigation of Few-Shot Learning for Fall Detection

Bakshi¹

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Falls affect seniors' quality of life, and therefore fall detection and prevention are paramount for the health and safety of aging seniors. Current deep learning-based fall detection methods perform well when a large amount of training data is available. As obtaining fall data from seniors is extremely difficult, training deep learning models is a challenge, and therefore, a few-shot Siamese network is considered in this thesis. A shallow 1 × 1 convolutional neural network for Siamese and Triplet networks is proposed in this work. A deeper architecture-based on the Inception and Densenet networks is also considered to improve the fall detection performance. The performances of the proposed few-shot Siamese architectures and Triplet networks are investigated using signals obtained from a wearable sensor. The proposed learning models outperform the traditional deep learning networks, while Siamese architectures also demonstrate generalizability by classifying unseen classes of falls and falls from different sensing modalities. First and foremost I am extremely grateful to my supervisor, Dr. Sreeraman Rajan, for his awesome advice, continuous support, and patience during the course of my masters program. His plentiful experience have encouraged me through out the time of my research and daily life. I thank the examination committee members for their time and efforts to evaluate my work and provide valuable feedback. I am extremely grateful to Dr. Jila Hosseinkhani for meticulously proof-reading my thesis and providing appropriate feedback. I would like to thank the Department of Systems and Computer Engineering, Carleton University, for providing me the opportunity and on-demand support for the completion of my program. I also thank the Natural Science and Engineering Council of Canada (NSERC) for partially funding the work. I also acknowledge the financial support that I got for a term from Department of National Defence's (DND)-IDEAS-Micronet initiative. These financial supports have made my study and life in Canada a smooth experience through out the ups and downs of this pandemic-ridden world. Finally, I would like to express my gratitude to my parents. Without their financial support, tremendous understanding and encouragement in the past few months, it would be impossible for me to complete my program.

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Detection and multi-class classification of falling in elderly people by deep belief network algorithms

Cited by 25 publications

References 50 publications

Classification of Indoor Human Fall Events Using Deep Learning

Classification of Indoor Human Fall Events Using Deep Learning

Entropy Measurement to Extract the Signification of Abnormal Activity from Camera’s Frames and its Application for Fall Detection

Investigation of Few-Shot Learning for Fall Detection

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