Quantifying the Effects of Temperature and Noise on Attention-Level Using EDA and EEG Sensors

Xue, Zhengrui; Yang, Luning; Rattadilok, Prapa; Li, Shanshan; Gao, Longyue

doi:10.1007/978-3-030-32962-4_23

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…The power spectral density was calculated by the Lomb-Scargle periodogram [60]. Based on original research articles [31,[35][36][37]42,47,[61][62][63], 14 HRV features listed in Table 1 that might be the potential to differentiate concentration and distraction were computed.…”

Section: Physiological Signal Preprocessing and Feature Computationmentioning

confidence: 99%

Monitoring Inattention in Construction Workers Caused by Physical Fatigue Using Electrocardiograph (ECG) and Galvanic Skin Response (GSR) Sensors

Ouyang,

Liu,

Cheng

et al. 2023

Sensors

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Physical fatigue is frequent for heavy manual laborers like construction workers, but it causes distraction and may lead to safety incidents. The purpose of this study is to develop predictive models for monitoring construction workers’ inattention caused by physical fatigue utilizing electrocardiograph (ECG) and galvanic skin response (GSR) sensors. Thirty participants were invited to complete an attention-demanding task under non-fatigued and physically fatigued conditions. Supervised learning algorithms were utilized to develop models predicting their attentional states, with heart rate variability (HRV) features derived from ECG signals and skin electric activity features derived from GSR signals as data inputs. The results demonstrate that using HRV features alone could obtain a prediction accuracy of 88.33%, and using GSR features alone could achieve an accuracy of 76.67%, both through the KNN algorithm. The accuracy increased to 96.67% through the SVM algorithm when combining HRV and GSR features. The findings indicate that ECG sensors used alone or in combination with GSR sensors can be applied to monitor construction workers’ inattention on job sites. The findings would provide an approach for detecting distracted workers at job sites. Additionally, it might reveal the relationships between workers’ physiological features and attention.

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Section: Physiological Signal Preprocessing and Feature Computationmentioning

confidence: 99%

Monitoring Inattention in Construction Workers Caused by Physical Fatigue Using Electrocardiograph (ECG) and Galvanic Skin Response (GSR) Sensors

Ouyang,

Liu,

Cheng

et al. 2023

Sensors

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“…The physiological measures include skin conductivity, heart rate and hand movements, which can be easily measured using available devices. Some studies have used electroencephalography (EEG) sensors to infer attentional level in ASD [18]. However, as this study conducted the data collection in a real school setting where the acceptance of EEG headband by children and parents was low at the parental consent stage, EEG sensor was not involved in this system.…”

Section: Monitoring System Developmentmentioning

confidence: 99%

A Machine Learning-Based Monitoring System for Attention and Stress Detection for Children with Autism Spectrum Disorders

Deng

Rattadilok

Xiong

2021

Proceedings of the 2021 International Conference on Intelligent Medicine and Health

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The majority of children with Autism Spectrum Disorders (ASD) have faced difficulties in sensory processing, which affect their ability of effective attention and stress management. Children with ASD also have unique patterns of sensory processing when responding to the stimuli in the environment. In this study, a real-time monitoring system has been designed and developed for attention and stress detection. Comprehensive sensory information, including environmental, physiological, and sensory profile data can be collected by the system using sensors, smart devices, and a standard sensory profiling questionnaire. Data acquisition with 35 ASD children using the system prototype was successfully conducted. With the acquired data set, different machine learning models were trained to predict attentional and stress level. Among all the investigated models, Gradient Boosting Decision Tree and Random Forest obtained the best prediction accuracies of 86.67% and 99.05% on attention and stress detection respectively. The two models were then implemented into the system for automatic detection. Future work could be focusing on exploring more supportive features to improve the prediction accuracy for attention detection. Such an easily-accessed monitoring system tailored for children with ASD could be widely-used in daily life to assist ASD users with their attention and stress management. CCS CONCEPTS •Human-centered computing~Accessibility~Accessibility technologies • Applied computing~Life and medical sciences~Health informatics • Social and professional topics~User characteristics~People with disabilities

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Quantifying the Effects of Temperature and Noise on Attention-Level Using EDA and EEG Sensors

Cited by 2 publications

References 25 publications

Monitoring Inattention in Construction Workers Caused by Physical Fatigue Using Electrocardiograph (ECG) and Galvanic Skin Response (GSR) Sensors

Monitoring Inattention in Construction Workers Caused by Physical Fatigue Using Electrocardiograph (ECG) and Galvanic Skin Response (GSR) Sensors

A Machine Learning-Based Monitoring System for Attention and Stress Detection for Children with Autism Spectrum Disorders

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