Fall Behavior Recognition Based on Deep Learning and Image Processing

Xu, He; Shen, Leixian; Zhang, Qingyun; Cao, Guoxu

doi:10.4018/ijmcmc.2018100101

Cited by 7 publications

(2 citation statements)

References 40 publications

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“…For instance, in [50], a vision-based approach for detecting falls was discussed, which relied on the temporal gradualness norm. Similarly, the system in [51] employed a DeeperCut model to generate features by extracting the human skeleton and decomposing it into five parts, including two legs, two arms, and one head. The five parts generated 14 data points, which were used as input features for a deep classifier.…”

Section: Vision-based Sensorsmentioning

confidence: 99%

A Robust and Automated Vision-Based Human Fall Detection System Using 3D Multi-Stream CNNs with an Image Fusion Technique

2023

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Unintentional human falls, particularly in older adults, can result in severe injuries and death, and negatively impact quality of life. The World Health Organization (WHO) states that falls are a significant public health issue and the primary cause of injury-related fatalities worldwide. Injuries resulting from falls, such as broken bones, trauma, and internal injuries, can have severe consequences and can lead to a loss of mobility and independence. To address this problem, there have been suggestions to develop strategies to reduce the frequency of falls, in order to decrease healthcare costs and productivity loss. Vision-based fall detection approaches have proven their effectiveness in addressing falls on time, which can help to reduce fall injuries. This paper introduces an automated vision-based system for detecting falls and issuing instant alerts upon detection. The proposed system processes live footage from a monitoring surveillance camera by utilizing a fine-tuned human segmentation model and image fusion technique as pre-processing and classifying a set of live footage with a 3D multi-stream CNN model (4S-3DCNN). The system alerts when the sequence of the Falling of the monitored human, followed by having Fallen, takes place. The effectiveness of the system was assessed using the publicly available Le2i dataset. System validation revealed an impressive result, achieving an accuracy of 99.44%, sensitivity of 99.12%, specificity of 99.12%, and precision of 99.59%. Based on the reported results, the presented system can be a valuable tool for detecting human falls, preventing fall injury complications, and reducing healthcare and productivity loss costs.

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Section: Vision-based Sensorsmentioning

confidence: 99%

A Robust and Automated Vision-Based Human Fall Detection System Using 3D Multi-Stream CNNs with an Image Fusion Technique

2023

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“…Slow feature analysis (SFA) used high-level semantic contents and compared them with the posture of the fall incident (six shape features) extracted from the covered silhouette. In a system described in [ 42 ], features were generated using a DeeperCut model. The model extracted the human skeleton and decomposed it into five parts—two arms, two legs, and one head—which were used to generate 14 data points.…”

Section: Related Workmentioning

confidence: 99%

Human Fall Detection Using 3D Multi-Stream Convolutional Neural Networks with Fusion

Alanazi

Muhammad

2022

Diagnostics

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Human falls, especially for elderly people, can cause serious injuries that might lead to permanent disability. Approximately 20–30% of the aged people in the United States who experienced fall accidents suffer from head trauma, injuries, or bruises. Fall detection is becoming an important public healthcare problem. Timely and accurate fall incident detection could enable the instant delivery of medical services to the injured. New advances in vision-based technologies, including deep learning, have shown significant results in action recognition, where some focus on the detection of fall actions. In this paper, we propose an automatic human fall detection system using multi-stream convolutional neural networks with fusion. The system is based on a multi-level image-fusion approach of every 16 frames of an input video to highlight movement differences within this range. This results of four consecutive preprocessed images are fed to a new proposed and efficient lightweight multi-stream CNN model that is based on a four-branch architecture (4S-3DCNN) that classifies whether there is an incident of a human fall. The evaluation included the use of more than 6392 generated sequences from the Le2i fall detection dataset, which is a publicly available fall video dataset. The proposed method, using three-fold cross-validation to validate generalization and susceptibility to overfitting, achieved a 99.03%, 99.00%, 99.68%, and 99.00% accuracy, sensitivity, specificity, and precision, respectively. The experimental results prove that the proposed model outperforms state-of-the-art models, including GoogleNet, SqueezeNet, ResNet18, and DarkNet19, for fall incident detection.

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