Deep CNN model based on serial-parallel structure optimization for four-class motor imagery EEG classification

Biomedical Signal Processing and Control, Journal Year: 2021, Volume and Issue: 72, P. 103338 - 103338

Published: Nov. 12, 2021

Language: Английский

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

Diagnostics, Journal Year: 2022, Volume and Issue: 12(12), P. 3060 - 3060

Published: Dec. 6, 2022

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

Language: Английский

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Electroencephalogram-Based Motor Imagery Signals Classification Using a Multi-Branch Convolutional Neural Network Model with Attention Blocks

Bioengineering, Journal Year: 2022, Volume and Issue: 9(7), P. 323 - 323

Published: July 18, 2022

Brain signals can be captured via electroencephalogram (EEG) and used in various brain-computer interface (BCI) applications. Classifying motor imagery (MI) using EEG is one of the important applications that help a stroke patient to rehabilitate or perform certain tasks. Dealing with EEG-MI challenging because are weak, may contain artefacts, dependent on patient's mood posture, have low signal-to-noise ratio. This paper proposes multi-branch convolutional neural network model called Multi-Branch EEGNet Convolutional Block Attention Module (MBEEGCBAM) attention mechanism fusion techniques classify signals. The applied both channel-wise spatial-wise. proposed lightweight has fewer parameters higher accuracy compared other state-of-the-art models. 82.85% 95.45% BCI-IV2a dataset high gamma dataset, respectively. Additionally, when approach (FMBEEGCBAM), it achieves 83.68% 95.74% accuracy,

Language: Английский

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

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(12), P. 6916 - 6916

Published: June 7, 2023

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

Language: Английский

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EEG-based motor imagery classification with quantum algorithms

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 247, P. 123354 - 123354

Published: Jan. 30, 2024

Language: Английский

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