Computer vision based fall detection by a convolutional neural network

Yu, Miao; Gong, Liyun; Kollias, Stefanos

doi:10.1145/3136755.3136802

Cited by 47 publications

(29 citation statements)

References 16 publications

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“…Yhdego et al [65] proposed pre-trained kinematics based machine learning approach in the annotated accelerometry datasets. Another application of CNN is applied by Yu et al [66], where background subtraction method is applied to extract the human body silhouette. In the proposed system, CNN is applied to pre-processed extracted silhouettes that corresponds to human movement like standing, sitting, bending and lying down.…”

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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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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“…To show the performance of our feature extraction method for posture recognition. We compare our method with CNN [ 31 ], ellipse descriptor [ 19 ] and shape context [ 52 ] using Dataset D1 and Dataset D2. Table 8 refers to result using D1 and Table 9 refers to result using D2.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

“…First, they extracted human silhouettes from video frames in order to train DBN for posture classification, then, they adopted a rule-based method for fall confirmation. The fall is confirmed when the lying posture events continue to occur for longer than 30 s. In [ 31 ], the authors used the Convolution Neural Network (CNN) for posture classification. The results obtained by these two methods were conducted on the same dataset [ 17 ], and CNN achieved higher accuracy than DBN.…”

Section: Related Workmentioning

confidence: 99%

Fall Detection System-Based Posture-Recognition for Indoor Environments

2021

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The majority of the senior population lives alone at home. Falls can cause serious injuries, such as fractures or head injuries. These injuries can be an obstacle for a person to move around and normally practice his daily activities. Some of these injuries can lead to a risk of death if not handled urgently. In this paper, we propose a fall detection system for elderly people based on their postures. The postures are recognized from the human silhouette which is an advantage to preserve the privacy of the elderly. The effectiveness of our approach is demonstrated on two well-known datasets for human posture classification and three public datasets for fall detection, using a Support-Vector Machine (SVM) classifier. The experimental results show that our method can not only achieves a high fall detection rate but also a low false detection.

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“…In an online pipeline aiming towards real-time action recognition, per-frame classification is central. Previous studies, such as the one in [30], perform classification between unintentional falls and other activities on single postures/frames, rather than complete sequences. The methodology presented in this paper performs online action analysis, while taking into account the history of every previous per-frame classification task.…”

Section: A Vgg Cnn-based Pipelinementioning

confidence: 99%

A Compact Sequence Encoding Scheme for Online Human Activity Recognition in HRI Applications

Tsatiris

Karpouzis

Kollias

2020

Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference

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Human activity recognition and analysis has always been one of the most active areas of pattern recognition and machine intelligence, with applications in various fields, including but not limited to exertion games, surveillance, sports analytics and healthcare. Especially in Human-Robot Interaction, human activity understanding plays a crucial role as household robotic assistants are a trend of the near future. However, state-of-the-art infrastructures that can support complex machine intelligence tasks are not always available, and may not be for the average consumer, as robotic hardware is expensive. In this paper we propose a novel action sequence encoding scheme which efficiently transforms spatio-temporal action sequences into compact representations, using Mahalanobis distance-based shape features and the Radon transform. This representation can be used as input for a lightweight convolutional neural network. Experiments show that the proposed pipeline, when based on state-of-the-art human pose estimation techniques, can provide a robust end-to-end online action recognition scheme, deployable on hardware lacking extreme computing capabilities.

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Computer vision based fall detection by a convolutional neural network

Cited by 47 publications

References 16 publications

Deep Learning Based Systems Developed for Fall Detection: A Review

Deep Learning Based Systems Developed for Fall Detection: A Review

Fall Detection System-Based Posture-Recognition for Indoor Environments

A Compact Sequence Encoding Scheme for Online Human Activity Recognition in HRI Applications

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