Towards Musculoskeletal Simulation-Aware Fall Injury Mitigation: Transfer Learning with Deep CNN for Fall Detection

Yhdego, Haben; Li, Jiang; Morrison, Steven; Audette, Michel; Paolini, Christopher; Sarkar, Mitali; Okhravi, Hamid

doi:10.23919/springsim.2019.8732857

Cited by 30 publications

(21 citation statements)

References 18 publications

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“…The lowest accuracy obtained 74% in [63] using CNN. The moderate accuracy of the reviewed system is 96.43% found in [65].…”

Section: A Discussionmentioning

confidence: 91%

“…However, the performance was not measured for real-life elderly falling event in different viewpoints and environment. 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.…”

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

confidence: 99%

“…Various datasets have been used in the reviewed systems. Some of them used publicly available dataset such as KTH dataset [85], Uni-MiB SHAR [75], [77], [79] SisFall [70], [77], [79] UMA fall [79], UP-Fall [78], Multicam [78], URFD [64], [65], [73] and [76]. Some of them used simulated datasets generated by trainers.…”

Section: )mentioning

confidence: 99%

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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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“…The lowest accuracy obtained 74% in [63] using CNN. The moderate accuracy of the reviewed system is 96.43% found in [65].…”

Section: A Discussionmentioning

confidence: 91%

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

confidence: 99%

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Deep Learning Based Systems Developed for Fall Detection: A Review

et al. 2020

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“…Importantly, the use of recent trends and advanced technology in healthcare industries have gain a crucial role [3][4][5]. However, achieve high safety living [6][7][8] for elderly and patients is challenged. Thus, healthcare monitoring/tracking for those people are vital [9].…”

Section: Introductionmentioning

confidence: 99%

A review of wearable sensors based monitoring with daily physical activity to manage type 2 diabetes

AlShorman

Alshorman²,

Alkahtani

2021

IJECE

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Globally, the aging and the lifestyle lead to rabidly increment of the number of type two diabetes (T2D) patients. Critically, T2D considers as one of the most challenging healthcare issue. Importantly, physical activity (PA) plays a vital role of improving glycemic control T2D. However, daily monitoring of T2D using wearable devices/ sensors have a crucial role to monitor glucose levels in the blood. Nowadays, daily physical activity (PA) and exercises have been used to manage T2D. The main contribution of the proposed study is to review the literature about managing and monitoring T2D with daily PA through wearable devices and sensors. Finally, challenges and future trends are also highlighted.

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“…Different from traditional vision-based fall detection methods relying on hand-crafted features, vision-based fall detection methods using deep learning networks can automatically extract features for detection after learning and analyzing a mass of data, and hence have recently received widespread attention [5], [6]. Deep networks have been increasingly applied to fall detection [7]- [11]. However, information may be lost after multiple layers of a deep network, which will lead to representativeness reduction of the feature in the network and further affect the accuracy of fall detection.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Fall Detection With Multi-Task Hourglass Convolutional Auto-Encoder

Liu

et al. 2020

IEEE Access

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Fall detection is a hot research issue in intelligent video surveillance. Falls can generate physical and psychological damage, especially for the elderly. Different from most conventional vision-based fall detection methods typically relying on hand-crafted features, fall detection methods based on deep learning techniques can automatically learn features and hence have got widespread concern recently. However, as deep networks are increasingly applied to fall detection, the problem of information loss in the deep networks can not be ignored, because this will ultimately affect the performance of fall detection. To solve the above problem, we propose a vision-based fall detection method using multi-task hourglass convolutional auto-encoder (HCAE). In this method, hourglass residual units (HRUs) are introduced into the encoder of the HCAE to extract multiscale features by expanding receptive fields of neurons. A multi-task mechanism is presented to enhance the feature representativeness of the network by completing an auxiliary task of frame reconstruction while realizing the main task of fall detection. Experimental results demonstrate that, the proposed method can effectively achieve accurate fall detection with the shallow-layer network, and outperforms several state-of-the-art methods. INDEX TERMS Fall detection, deep learning, hourglass convolutional auto-encoder (HCAE), hourglass residual unit, multi-task mechanism.

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Towards Musculoskeletal Simulation-Aware Fall Injury Mitigation: Transfer Learning with Deep CNN for Fall Detection

Cited by 30 publications

References 18 publications

Deep Learning Based Systems Developed for Fall Detection: A Review

Deep Learning Based Systems Developed for Fall Detection: A Review

A review of wearable sensors based monitoring with daily physical activity to manage type 2 diabetes

Vision-Based Fall Detection With Multi-Task Hourglass Convolutional Auto-Encoder

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