Activity recognition for indoor fall detection using convolutional neural network

Adhikari, Kripesh; Bouchachia, Hamid; Nait-Charif, Hammadi

doi:10.23919/mva.2017.7986795

Cited by 114 publications

(67 citation statements)

References 7 publications

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“…Adhikari et al [63] proposed a fall detection system based on video images taken from RGB-Depth camera of Kinect. CNN was used in the system to recognize ADL and fall events.…”

Section: A Convolutional Neural Network (Cnn) Based Fall Detection Smentioning

confidence: 99%

Deep Learning Based Systems Developed for Fall Detection: A Review

et al. 2020

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Accidental falls are a major source of loss of autonomy, deaths, and injuries among the elderly. Accidental falls also have a remarkable impact on the costs of national health systems. Thus, extensive research and development of fall detection and rescue systems are a necessity. Technologies related to fall detection should be reliable and effective to ensure a proper response. This paper provides a comprehensive review on state-of-the-art fall detection technologies considering the most powerful deep learning methodologies. We reviewed the most recent and effective deep learning methods for fall detection and categorized them into three categories: Convolutional Neural Network (CNN) based systems, Long Short-Term Memory (LSTM) based systems, and Auto-encoder based systems. Among the reviewed systems, three dimensional (3D) CNN, CNN with 10-fold cross-validation, LSTM with CNN based systems performed the best in terms of accuracy, sensitivity, specificity, etc. The reviewed systems were compared based on their working principles, used deep learning methods, used datasets, performance metrics, etc. This review is aimed at presenting a summary and comparison of existing state-of-the-art deep learning based fall detection systems to facilitate future development in this field.

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“…Adhikari et al [63] proposed a fall detection system based on video images taken from RGB-Depth camera of Kinect. CNN was used in the system to recognize ADL and fall events.…”

Section: A Convolutional Neural Network (Cnn) Based Fall Detection Smentioning

confidence: 99%

Deep Learning Based Systems Developed for Fall Detection: A Review

et al. 2020

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“…A strategy similar to the one presented in this work, using RGB-D (reg, green, blue plus depth) images, colour subtraction, and a CNN, was presented for the identification of fall events [24]. The difference lies in the source of the images.…”

Section: Resultsmentioning

confidence: 99%

“…Regarding the image-based approach, the use of the Microsoft Kinect device is a solution often found in the literature [12,[23][24][25]. This sensor simplifies some tasks like background subtraction and/or skeleton generation but limits or reduces the accuracy after a few meters of distance [26].…”

Section: Related Workmentioning

confidence: 99%

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Heart Attack Detection in Colour Images Using Convolutional Neural Networks

et al. 2019

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Cardiovascular diseases are the leading cause of death worldwide. Therefore, getting help in time makes the difference between life and death. In many cases, help is not obtained in time when a person is alone and suffers a heart attack. This is mainly due to the fact that pain prevents him/her from asking for help. This article presents a novel proposal to identify people with an apparent heart attack in colour images by detecting characteristic postures of heart attack. The method of identifying infarcts makes use of convolutional neural networks. These have been trained with a specially prepared set of images that contain people simulating a heart attack. The promising results in the classification of infarcts show 91.75% accuracy and 92.85% sensitivity.

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“…K. Tra et al [13] used five features extracted from the ellipse model and input them to two HMM models to classify fall events and normal events. Adhikari et al [14] preprocess the RGB-D image, combined with the CNN network model to determine whether the human body fall, the detection accuracy can reach 81%. This paper proposes a new fall detection algorithm based on computer vision, which mainly uses a dual network structure.…”

Section: Introductionmentioning

confidence: 99%

A Computer Vision-Based Dual Network Approach for Indoor Fall Detection

Zhang¹,

Fang²,

Zhu³

2020

IJISRT

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In order to strengthen the monitoring of the elderly and reduce the safety risks caused by falls, a video-based indoor fall detection algorithm using a dual network structure is proposed. Firstly, for the recorded video stream, we apply the fine-tuned YOLACT network to extract the contours of the human body, and then design a simple convolutional neural network to distinguish the categories of different family activities (including bending, standing, sitting and lying), and finally make a fall decision. When a lying position is detected on the floor region, it is considered as a fall. Experiments show that the proposed algorithm can successfully detect fall events in different indoor scenarios, and have a low false detection rate on the constructed data set.

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Activity recognition for indoor fall detection using convolutional neural network

Cited by 114 publications

References 7 publications

Deep Learning Based Systems Developed for Fall Detection: A Review

Deep Learning Based Systems Developed for Fall Detection: A Review

Heart Attack Detection in Colour Images Using Convolutional Neural Networks

A Computer Vision-Based Dual Network Approach for Indoor Fall Detection

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