Classification of Indoor Human Fall Events Using Deep Learning

Sultana, Arifa; Deb, Kaushik; Dhar, Pranab Kumar; Koshiba, Takeshi

doi:10.3390/e23030328

Cited by 20 publications

(11 citation statements)

References 29 publications

(26 reference statements)

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“…In [ 24 ], the authors developed an architecture to classify human fall events using 2D CNN and GRU. Experimental results with this dataset show that the proposed model obtains an accuracy of

.…”

Section: Fall Detection Datasets and Related Workmentioning

confidence: 99%

Human Activity Recognition by Sequences of Skeleton Features

Ramirez

Velastín

Aguayo

et al. 2022

Sensors

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In recent years, much effort has been devoted to the development of applications capable of detecting different types of human activity. In this field, fall detection is particularly relevant, especially for the elderly. On the one hand, some applications use wearable sensors that are integrated into cell phones, necklaces or smart bracelets to detect sudden movements of the person wearing the device. The main drawback of these types of systems is that these devices must be placed on a person’s body. This is a major drawback because they can be uncomfortable, in addition to the fact that these systems cannot be implemented in open spaces and with unfamiliar people. In contrast, other approaches perform activity recognition from video camera images, which have many advantages over the previous ones since the user is not required to wear the sensors. As a result, these applications can be implemented in open spaces and with unknown people. This paper presents a vision-based algorithm for activity recognition. The main contribution of this work is to use human skeleton pose estimation as a feature extraction method for activity detection in video camera images. The use of this method allows the detection of multiple people’s activities in the same scene. The algorithm is also capable of classifying multi-frame activities, precisely for those that need more than one frame to be detected. The method is evaluated with the public UP-FALL dataset and compared to similar algorithms using the same dataset.

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“…In [ 24 ], the authors developed an architecture to classify human fall events using 2D CNN and GRU. Experimental results with this dataset show that the proposed model obtains an accuracy of

.…”

Section: Fall Detection Datasets and Related Workmentioning

confidence: 99%

Human Activity Recognition by Sequences of Skeleton Features

Ramirez

Velastín

Aguayo

et al. 2022

Sensors

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show abstract

“…Lastly, sigmoid classification is utilized as binary classifier for human fall event detection. Sultana et al [14] aimed to identify fall event recognition in complex backgrounds depending upon visual data. Contrasting to most traditional background subtraction approaches that are based on background modeling.…”

Section: Related Workmentioning

confidence: 99%

Vision Based Real Time Monitoring System for Elderly Fall Event Detection Using Deep Learning

Priya¹

2022

Computer Systems Science and Engineering

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Human fall detection plays a vital part in the design of sensor based alarming system, aid physical therapists not only to lessen after fall effect and also to save human life. Accurate and timely identification can offer quick medical services to the injured people and prevent from serious consequences. Several vision-based approaches have been developed by the placement of cameras in diverse everyday environments. At present times, deep learning (DL) models particularly convolutional neural networks (CNNs) have gained much importance in the fall detection tasks. With this motivation, this paper presents a new vision based elderly fall event detection using deep learning (VEFED-DL) model. The proposed VEFED-DL model involves different stages of operations namely preprocessing, feature extraction, classification, and parameter optimization. Primarily, the digital video camera is used to capture the RGB color images and the video is extracted into a set of frames. For improving the image quality and eliminate noise, the frames are processed in three levels namely resizing, augmentation, and min-max based normalization. Besides, MobileNet model is applied as a feature extractor to derive the spatial features that exist in the preprocessed frames. In addition, the extracted spatial features are then fed into the gated recurrent unit (GRU) to extract the temporal dependencies of the human movements. Finally, a group teaching optimization algorithm (GTOA) with stacked autoencoder (SAE) is used as a binary classification model to determine the existence of fall or non-fall events. The GTOA is employed for the parameter optimization of the SAE model in such a way that the detection performance can be enhanced. In order to assess the fall detection performance of the presented VEFED-DL model, a set of simulations take place on the UR fall detection dataset and multiple cameras fall dataset. The experimental outcomes highlighted the superior performance of the presented method over the recent methods.

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“…In this study, we considered that these problems are approached from multiple perceptions [5]. For example, emphasize the use of the Machine Learning (ML) method employed to video audio processing, and image detection for detecting falls.…”

Section: Introductionmentioning

confidence: 99%

Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection System

Alluhaidan¹,

Alajmi²,

Al‐Wesabi³

et al. 2022

Computers, Materials &Amp; Continua

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Human fall detection (FD) acts as an important part in creating sensor based alarm system, enabling physical therapists to minimize the effect of fall events and save human lives. Generally, elderly people suffer from several diseases, and fall action is a common situation which can occur at any time. In this view, this paper presents an Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection (IAOA-DLFD) model to identify the fall/non-fall events. The proposed IAOA-DLFD technique comprises different levels of pre-processing to improve the input image quality. Besides, the IAOA with Capsule Network based feature extractor is derived to produce an optimal set of feature vectors. In addition, the IAOA uses to significantly boost the overall FD performance by optimal choice of CapsNet hyperparameters. Lastly, radial basis function (RBF) network is applied for determining the proper class labels of the test images. To showcase the enhanced performance of the IAOA-DLFD technique, a wide range of experiments are executed and the outcomes stated the enhanced detection outcome of the IAOA-DLFD approach over the recent methods with the accuracy of 0.997.

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Classification of Indoor Human Fall Events Using Deep Learning

Cited by 20 publications

References 29 publications

Human Activity Recognition by Sequences of Skeleton Features

Human Activity Recognition by Sequences of Skeleton Features

Vision Based Real Time Monitoring System for Elderly Fall Event Detection Using Deep Learning

Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection System

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