Development of a comprehensive fatigue detection model for beekeeping activities based on deep learning and EEG signals
Authors:
- Pingan WangPWPingan Wang
- Ju-Seok NamJNJu-Seok Nam
- Xiongzhe HanXHXiongzhe Han
- 25 July 2024
- journal article
- research article
- Published by Elsevier in Computers and Electronics in Agriculture
- Vol. 225
Abstract
No abstract availableKeywords
Funding Information
- National Academy of Agricultural Sciences
- Rural Development Administration (RS-2024-00343766)
This publication has 49 references indexed in Scilit:
- Development, analysis, and verification of an intelligent auxiliary beekeeping device mounted on a crawler transporterComputers and Electronics in Agriculture, 2023
- Aging-induced alterations in EEG spectral power associated with graded force motor tasksExperimental Brain Research, 2023
- Three-dimensional DenseNet self-attention neural network for automatic detection of student’s engagementApplied Intelligence, 2022
- Causes and consequences of sleep loss and fatigue: The worker perspective in the coral reef tourism industryAnnals of Tourism Research, 2021
- Development and recovery time of mental fatigue and its impact on motor functionBiological Psychology, 2021
- Mental Fatigue and Sport-Specific Psychomotor Performance: A Systematic ReviewSports Medicine, 2021
- Effects of physical fatigue on the induction of mental fatigue of construction workers: A pilot study based on a neurophysiological approachAutomation in Construction, 2020
- Inter-subject transfer learning for EEG-based mental fatigue recognitionAdvanced Engineering Informatics, 2020
- Kubios HRV – Heart rate variability analysis softwareComputer Methods and Programs in Biomedicine, 2014
- Spherical splines for scalp potential and current density mappingElectroencephalography and Clinical Neurophysiology, 1989