An Automated Fall Detection System Using Recurrent Neural Networks

Luna-Perejón, Francisco; Civit-Masot, Javier; Amaya-Rodriguez, Isabel; Durán-López, Lourdes; Domínguez-Morales, Juan Pedro; Balcells, A; Linares-Barranco, A.

doi:10.1007/978-3-030-21642-9_6

Cited by 14 publications

(12 citation statements)

References 10 publications

(13 reference statements)

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“…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]. Comparing both categories, vision based systems provide better result than sensor based system.…”

Section: A Discussionmentioning

confidence: 99%

“…Real-time monitoring is another important factor in fall detection systems, as fall events occur in a very short period of time and quick response after fall events are paramount to patient survivability. Out of the reviewed systems, the systems proposed in [64], [67], [70], [72], [80], [81], [82], [85], [98], [100], [102], [104], and [105] have reported realtime performance.…”

Section: A Discussionmentioning

confidence: 99%

“…Perejon et al [104] introduced a fall detection method based on Gated RNN architectures along with LSTM and Gated Recurrent Units (GRU) taking accelerometer data as input which detects fall in real-time. The input is sent to a batch normalization layer, a dense end layer with a RNN layer for the prediction of fall events.…”

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%

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

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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“…Results obtained in [33] showed that the regularization of sample values substantially improves the effectiveness. To achieve this, a batch normalization layer is included at the beginning of the architecture.…”

Section: Methodsmentioning

confidence: 99%

Wearable Fall Detector Using Recurrent Neural Networks

Luna-Perejón

Domínguez-Morales

Balcells

2019

Sensors

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Falls have become a relevant public health issue due to their high prevalence and negative effects in elderly people. Wearable fall detector devices allow the implementation of continuous and ubiquitous monitoring systems. The effectiveness for analyzing temporal signals with low energy consumption is one of the most relevant characteristics of these devices. Recurrent neural networks (RNNs) have demonstrated a great accuracy in some problems that require analyzing sequential inputs. However, getting appropriate response times in low power microcontrollers remains a difficult task due to their limited hardware resources. This work shows a feasibility study about using RNN-based deep learning models to detect both falls and falls’ risks in real time using accelerometer signals. The effectiveness of four different architectures was analyzed using the SisFall dataset at different frequencies. The resulting models were integrated into two different embedded systems to analyze the execution times and changes in the model effectiveness. Finally, a study of power consumption was carried out. A sensitivity of 88.2% and a specificity of 96.4% was obtained. The simplest models reached inference times lower than 34 ms, which implies the capability to detect fall events in real-time with high energy efficiency. This suggests that RNN models provide an effective method that can be implemented in low power microcontrollers for the creation of autonomous wearable fall detection systems in real-time.

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