RGB-D Fall Detection via Deep Residual Convolutional LSTM Networks

Abobakr, Ahmed; Hossny, Mohammed; Abdel-Kader, Hala M.; Nahavandi, Saeid

doi:10.1109/dicta.2018.8615759

Cited by 34 publications

(20 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…As sensitivity is one of the criteria to evaluate any working model, some of the reviewed systems [63], [64], [70], [71], [73], [74], [77], [78], [80], [96], [98], [106] have mentioned it. From Figure 4, the highest sensitivity of the reviewed system is 100% found in [64], [98]. CNN with 10-fold cross-validation is used in [64] and Microsoft Kinect cameras is used for capturing fall event.…”

Section: A Discussionmentioning

confidence: 99%

“…CNN with 10-fold cross-validation is used in [64] and Microsoft Kinect cameras is used for capturing fall event. CNN, LSTM with ResNet, recurrent LSTM, logistic regression module is used in [98]. Both of the proposed system [64], [98] used same dataset named as UR fall detection dataset.…”

Section: A Discussionmentioning

confidence: 99%

“…The system has an on-line version with some drawbacks and can achieve 8 frames/second processing speed with GPU model GTX 1060 6G providing one output per frame. Abobakr et al [98] proposed an efficient, integrable and privacy-preserving fall detection system based on CNN and RNN. The system used images taken from Kinect RGB-D camera.…”

Section: ) Lstm With Recurrent Neural Network (Rnn) and Cnnmentioning

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%

See 4 more Smart Citations

Deep Learning Based Systems Developed for Fall Detection: A Review

et al. 2020

View full text Add to dashboard Cite

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.

show abstract