Visual guided deep learning scheme for fall detection

Lü, Na; Ren, Xiaodong; Song, Jinbo; Wu, Yidan

doi:10.1109/coase.2017.8256202

Cited by 13 publications

(17 citation statements)

References 36 publications

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“…In Tables I, II and III, we categorized all the systems that work with vision data and with various sensors data. Vision based systems work with the data from RGB and Depth camera of Kinect shown in [63], [64], [80], [81], [98], [100], [107], [108], RGB Cameras introduced in [67], [69], [76], [78], [82], [85], [96], [97], [108]. Sensor based systems work with accelerometer data shown in [70], [72], [74], [77], [79], [99], [102]- [106], gyroscope data depicted in [70], [104], radar data demonstrated in [69], [71], [116], [119].…”

Section: A Discussionmentioning

confidence: 99%

“…1D-CNN, 2D-CNN, 3D-CNN are described on Table I. 3D-CNN gives better performance in [96]. Some of them have used LSTM, RNN and RCN, Auto-encoder with three layers, regression, and logistic classifier.…”

Section: A Discussionmentioning

confidence: 99%

“…The systems in [64], [69], [74], [76], [77], [80], [82], [96] have also achieved high levels of accuracy using various deep learning methods. The highest accuracy achieved 99.98% in [64] using CNN with 10-fold crossvalidation, 99.85% in [69] using three CNN based on Alex-Net and SSD-Net, 99.84% in [96] using 3D CNN along with LSTM. The lowest accuracy obtained 74% in [63] using CNN.…”

Section: A Discussionmentioning

confidence: 99%

“…1) LSTM WITH 3D CNN A fall detection system based on LSTM with 3D CNN is developed by Lu et al [96] by overcoming the problems of existing methods like segmentation, image noise, variation, occlusion, and illumination. The system trained an automatic feature extractor only using kinetic data.…”

Section: B Long Short-term Memory (Lstm) Based Fall Detection Systemsmentioning

confidence: 99%

“…Almost, all of them are in the vision device category. URFD [98], [106], open video dataset [100], [108], sports-1M dataset [96] and multiple cameras fall dataset [96], MobiAct dataset [99], [102], [103], SisFall dataset [104], [105] are used and besides own dataset is used in [97], [107].…”

Section: ) Lstm With Recurrent Convolutional Network (Rcn) and Rnnmentioning

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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Section: A Discussionmentioning

confidence: 99%

Section: A Discussionmentioning

confidence: 99%

Section: A Discussionmentioning

confidence: 99%

Section: B Long Short-term Memory (Lstm) Based Fall Detection Systemsmentioning

confidence: 99%

Section: ) Lstm With Recurrent Convolutional Network (Rcn) and Rnnmentioning

confidence: 99%

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Deep Learning Based Systems Developed for Fall Detection: A Review

et al. 2020

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Hrnn4f: Hybrid Deep Random Neural Network for Multi-Channel Fall Activity Detection

Tahir¹,

Ahmad

Morison

et al. 2019

Prob. Eng. Inf. Sci.

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Falls are a major health concern in older adults. Falls lead to mortality, immobility and high costs to social and health care services. Early detection and classification of falls is imperative for timely and appropriate medical aid response. Traditional machine learning models have been explored for fall classification. While newly developed deep learning techniques have the ability to potentially extract high-level features from raw sensor data providing high accuracy and robustness to variations in sensor position, orientation and diversity of work environments that may skew traditional classification models. However, frequently used deep learning models like Convolutional Neural Networks (CNN) are computationally intensive. To the best of our knowledge, we present the first instance of a Hybrid Multichannel Random Neural Network (HMCRNN) architecture for fall detection and classification. The proposed architecture provides the highest accuracy of 92.23% with dropout regularization, compared to other deep learning implementations. The performance of the proposed technique is approximately comparable to a CNN yet requires only half the computation cost of the CNN-based implementation. Furthermore, the proposed HMCRNN architecture provides 34.12% improvement in accuracy on average than a Multilayer Perceptron.

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A Comparison of Machine Learning Approaches for Detecting Misogynistic Speech in Urban Dictionary

Lynn

Endo

Rosati

et al. 2019

2019 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (Cyber SA)

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Recent moves to consider misogyny as a hate crime have refocused efforts for owners of web properties to detect and remove misogynistic speech. This paper considers the use of deep learning techniques for detection of misogyny in Urban Dictionary, a crowdsourced online dictionary for slang words and phrases. We compare the performance of two deep learning techniques, Bi-LSTM and Bi-GRU, to detect misogynistic speech with the performance of more conventional machine learning techniques, logistic regression, Naive-Bayes classification, and Random Forest classification. We find that both deep learning techniques examined have greater accuracy in detecting misogyny in the Urban Dictionary than the other techniques examined.

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Visual guided deep learning scheme for fall detection

Cited by 13 publications

References 36 publications

Deep Learning Based Systems Developed for Fall Detection: A Review

Deep Learning Based Systems Developed for Fall Detection: A Review

Hrnn4f: Hybrid Deep Random Neural Network for Multi-Channel Fall Activity Detection

A Comparison of Machine Learning Approaches for Detecting Misogynistic Speech in Urban Dictionary

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