Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks

Santos, Guto Leoni; Endo, Patrícia Takako; Monteiro, Kayo Henrique de Carvalho; Rocha, Élisson da Silva; Silva, Ivanovitch; Lynn, Theo

doi:10.3390/s19071644

Cited by 183 publications

(94 citation statements)

References 24 publications

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“…The FARSEEING dataset was also taken into consideration by one of the two studies that employed three datasets: the work by Mauldin in [39], which introduced two similar databases (known as Smartwatch and Notch) collected with wrist sensors (a smartwatch and an external IMU-Inertial Measurement Unit-, respectively). Apart from the problems related to the difficulties of detecting falls with a wrist worn device, these databases, also used by Santos in [18], incorporate a moderate number of fall events that may hamper a thorough and systematic assessment of the effectiveness of the detector.…”

Section: Revision and Selection Of Public Datasetsmentioning

confidence: 99%

“…In the domain of machine learning, different classes of architectures based on artificial neural networks such as Recurrent Neural Networks (RNNs) [11][12][13] have been successfully employed as the movement classifier of a FDS. Similarly, Convolutional Neural Networks (CNNs) have also been recently proposed as a promising technology for those HAR (human activity recognition) systems [14,15] and wearable FDSs that process the data gathered by inertial sensors [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets

Casilari

Lora-Rivera

Garcìa-Lagos

2020

Sensors

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Due to the repercussion of falls on both the health and self-sufficiency of older people and on the financial sustainability of healthcare systems, the study of wearable fall detection systems (FDSs) has gained much attention during the last years. The core of a FDS is the algorithm that discriminates falls from conventional Activities of Daily Life (ADLs). This work presents and evaluates a convolutional deep neural network when it is applied to identify fall patterns based on the measurements collected by a transportable tri-axial accelerometer. In contrast with most works in the related literature, the evaluation is performed against a wide set of public data repositories containing the traces obtained from diverse groups of volunteers during the execution of ADLs and mimicked falls. Although the method can yield very good results when it is hyper-parameterized for a certain dataset, the global evaluation with the other repositories highlights the difficulty of extrapolating to other testbeds the network architecture that was configured and optimized for a particular dataset.

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Section: Revision and Selection Of Public Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets

Casilari

Lora-Rivera

Garcìa-Lagos

2020

Sensors

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“…In the last layer, the long short term memory (LSTM) layer is used. CNN-3B3Conv is proposed in [11] for human fall detection based on convolutional neural network. This model consists of three sequential blocks.…”

Section: Related Workmentioning

confidence: 99%

A New Approach for Thermal Vision based Fall Detection Using Residual Autoencoder

Elshwemy¹,

Elbasiony²,

Saidahmed³

2020

IJIES

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This paper focuses on falling of the elderly people which is considered as one of the most critical issue that can face them in their life. To deal with such issue, we propose a new approach named a Spatio-temporal Residual AutoEncoder (SRAE) model. This model is an unsupervised fall detector based on utilizing the deep learning technique to detect falls of the elderly people. Our proposed model uses autoencoder based on convolutional neural network, convolutional long short term memory (ConvLSTM) network, and residual connections to extract spatial and temporal features of videos captured from thermal cameras. The reconstruction error of an autoencoder is used to detect falls recorded in such thermal videos. Furthermore, SRAE model is tested on the publicly available thermal dataset where thermal images conserve the privacy of the elderly under observation which is a very important issue. The obtained results show that the our proposed model detects falls with high receiver operating characteristic area under curve (ROC AUC) (97%) ,and precision recall area under curve (PR AUC) (93%) compared to denoising autoencoder (DAE), convolutional autoencoder (CAE), and convolutional long short term memory autoencoder (CLSTMAE) introduced in the literature.

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“…Another CNN model composed of two convolutional and two max-pooling layers was used in [23] to detect falls and the results proved the CNN could achieve an accuracy of 98.61%. Furthermore, a CNN named CNN-3B3Conv was proposed in [24] to detect falls using acceleration measurements. The experiment results proved that the CNN-3B3Conv model could obtain much better results than recurrent neural networks with an accuracy near 99%.…”

Section: Introductionmentioning

confidence: 99%

“…The size of kernels used is 1 × 5 with a stride of 1. Furthermore, the first fully-connected layer consists of 512 neurons and the second layer consists of 8 neurons (change to 1 in this work) for classification.• (CNN-3B3Conv)[24]: CNN-3B3Conv consists of three-layer blocks. The first block consists of three convolutional layers and one max-pooling layer.…”

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confidence: 99%

Elderly Fall Detection with an Accelerometer Using Lightweight Neural Networks

Wang

et al. 2019

Electronics

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Falls have been one of the main threats to people’s health, especially for the elderly. Detecting falls in time can prevent the long lying time, which is extremely fatal. This paper intends to show the efficacy of detecting falls using a wearable accelerometer. In the past decade, the fall detection problem has been extensively studied. However, since the hardware resources of wearable devices are limited, designing highly accurate embeddable models with feasible computational cost remains an open research problem. In this paper, different types of shallow and lightweight neural networks, including supervised and unsupervised models are explored to improve the fall detection results. Experiment results on a large open dataset show that the lightweight neural networks proposed have obtained much better results than machine learning methods used in previous work. Moreover, the storage and computation requirements of these lightweight models are only a few hundredths of deep neural networks in literature. In tested lightweight neural networks, the best one is proved to be the supervised convolutional neural network (CNN) that can achieve an accuracy beyond 99.9% with only 441 parameters. Its storage and computation requirements are only 1.2 KB and 0.008 MFLOPs, which make it more suitable to be implemented in wearable devices with restricted memory size and computation power.

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Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks

Cited by 183 publications

References 24 publications

A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets

A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets

A New Approach for Thermal Vision based Fall Detection Using Residual Autoencoder

Elderly Fall Detection with an Accelerometer Using Lightweight Neural Networks

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