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基于贝叶斯-gcForest 的多模态特征融合脑疲劳识别系统

A Multimodal Feature Fusion Brain Fatigue Recognition System Based on Bayes-gcForest.

机构信息

College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China.

Shanghai Shentian Industrial Co., Ltd., Shanghai 200090, China.

出版信息

Sensors (Basel). 2024 May 2;24(9):2910. doi: 10.3390/s24092910.

DOI:10.3390/s24092910
PMID:38733015
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11086115/
Abstract

Modern society increasingly recognizes brain fatigue as a critical factor affecting human health and productivity. This study introduces a novel, portable, cost-effective, and user-friendly system for real-time collection, monitoring, and analysis of physiological signals aimed at enhancing the precision and efficiency of brain fatigue recognition and broadening its application scope. Utilizing raw physiological data, this study constructed a compact dataset that incorporated EEG and ECG data from 20 subjects to index fatigue characteristics. By employing a Bayesian-optimized multi-granularity cascade forest (Bayes-gcForest) for fatigue state recognition, this study achieved recognition rates of 95.71% and 96.13% on the DROZY public dataset and constructed dataset, respectively. These results highlight the effectiveness of the multi-modal feature fusion model in brain fatigue recognition, providing a viable solution for cost-effective and efficient fatigue monitoring. Furthermore, this approach offers theoretical support for designing rest systems for researchers.

摘要

现代社会越来越认识到脑疲劳是影响人类健康和生产力的关键因素。本研究引入了一种新颖、便携、经济高效且用户友好的系统，用于实时采集、监测和分析生理信号，旨在提高脑疲劳识别的精度和效率，并拓宽其应用范围。本研究利用原始生理数据构建了一个紧凑的数据集，其中包含 20 名受试者的 EEG 和 ECG 数据，以指标疲劳特征。通过使用贝叶斯优化多粒度级联森林（Bayes-gcForest）进行疲劳状态识别，本研究在 DROZY 公共数据集和构建数据集上分别实现了 95.71%和 96.13%的识别率。这些结果突出了多模态特征融合模型在脑疲劳识别中的有效性，为经济高效和有效的疲劳监测提供了可行的解决方案。此外，该方法为研究人员设计休息系统提供了理论支持。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0464/11086115/6e9404e7f091/sensors-24-02910-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0464/11086115/007132a7fe86/sensors-24-02910-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0464/11086115/bd57700a8fd0/sensors-24-02910-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0464/11086115/d414a4f99afd/sensors-24-02910-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0464/11086115/c9f26f15a1ee/sensors-24-02910-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0464/11086115/d433cc51dcd1/sensors-24-02910-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0464/11086115/8e4b1e0623f1/sensors-24-02910-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0464/11086115/b7b2a354c2a2/sensors-24-02910-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0464/11086115/d2125e0254bb/sensors-24-02910-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0464/11086115/e4daeb4e20d3/sensors-24-02910-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0464/11086115/6e9404e7f091/sensors-24-02910-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0464/11086115/007132a7fe86/sensors-24-02910-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0464/11086115/bd57700a8fd0/sensors-24-02910-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0464/11086115/d414a4f99afd/sensors-24-02910-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0464/11086115/c9f26f15a1ee/sensors-24-02910-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0464/11086115/d433cc51dcd1/sensors-24-02910-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0464/11086115/8e4b1e0623f1/sensors-24-02910-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0464/11086115/b7b2a354c2a2/sensors-24-02910-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0464/11086115/d2125e0254bb/sensors-24-02910-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0464/11086115/e4daeb4e20d3/sensors-24-02910-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0464/11086115/6e9404e7f091/sensors-24-02910-g010.jpg

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