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

You Zhou, Pukun Chen, Yifan Fan, Yin Wu

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

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

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

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

Yin WuCollege of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China.ORCID 0000-0001-8473-8990

Funding

Jiangsu Provincial Government Scholarship for Overseas Studies JS-2018-043National Natural Science Foundation of China 31700478National Natural Science Foundation of China: 32171788

6 · The paper itself

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.

Indexed as

Bayes TheoremElectroencephalographyAdultAlgorithmsBrainElectrocardiographyFatigueFemaleHumansMaleSignal Processing, Computer-AssistedYoung AdultBayes-gcForestECGEEGfatigue recognitionfeature fusion

Identifiers

PMID38733015

DOI10.3390/s24092910

PMCPMC11086115

What OpenQuestion holds

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.