Abstract This study attempted to multimodally measure mental workload and validate indicators for estimating mental workload. A simulated computer work composed of mental arithmetic tasks with different levels of difficulty was designed and used in the experiment to measure physiological signals (heart rate, heart rate variability, electromyography, electrodermal activity, and respiration), subjective ratings of mental workload (the NASA Task Load Index), and task performance. The indices from electrodermal activity and respiration had a significant increment as task difficulty increased. There were no significant differences between the average heart rate and the low-frequency/high-frequency ratio among tasks. The classification of mental workload using combined indices as inputs showed that classification models combining physiological signals and task performance can reach satisfying accuracy at 96.4% and an accuracy of 78.3% when only using physiological indices as inputs. The present study also showed that ECG and EDA signals have good discriminating power for mental workload detection. Practitioner summary: The methods used in this study could be applied to office workers, and the findings provide preliminary support and theoretical exploration for follow-up early mental workload detection systems, whose implementation in the real world could beneficially impact worker health and company efficiency. Abbreviations: NASA-TLX: the national aeronautics and space administration-task load index; ECG: electrocardiographic; EDA: electrodermal activity; EEG: electroencephalogram; LDA: linear discriminant analysis; SVM: support vector machine; KNN: k-nearest neighbor; ANNs: artificial neural networks; EMG: electromyography; PPG: photoplethysmography; SD: standard deviation; BMI: body mass index; DSSQ: dundee stress state questionnaire; ANOVA: analysis of variance; SC: skin conductance; RMS: root mean square; AVHR: the average heart rate; HR: heart rate; LF/HF: the ratio between the low frequencies band and the high frequency band; PSD: power spectral density; MF: median frequency; HRV: heart rate variability; BPNN: backpropagation neural network

中文翻译：

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用多模态方法和机器学习测量和识别模拟计算机任务中的心理负荷

摘要 本研究试图多模态测量脑力负荷并验证估算脑力负荷的指标。设计了一个由不同难度的心算任务组成的模拟计算机工作，并在实验中用于测量生理信号（心率、心率变异性、肌电图、皮肤电活动和呼吸）、心理工作量的主观评级（美国宇航局任务负载指数）和任务性能。随着任务难度的增加，来自皮肤电活动和呼吸的指数有显着增加。任务间的平均心率和低频/高频比之间没有显着差异。以组合指标为输入的脑力负荷分类结果表明，结合生理信号和任务表现的分类模型可以达到令人满意的准确率，达到96.4%，仅以生理指标为输入的准确率达到78.3%。本研究还表明，心电图和 EDA 信号对脑力负荷检测具有良好的判别能力。从业者总结：本研究中使用的方法可以应用于上班族，研究结果为后续早期心理工作量检测系统提供了初步支持和理论探索，其在现实世界中的实施可以有益地影响员工健康和公司效率。缩写：NASA-TLX：美国国家航空航天局-任务负荷指数；心电图：心电图；EDA：皮肤电活动；EEG：脑电图；LDA：线性判别分析；SVM：支持向量机；KNN：k-最近邻；ANNs：人工神经网络；EMG：肌电图；PPG：光电容积脉搏波；SD：标准差；BMI：体重指数；DSSQ：邓迪压力状态问卷；ANOVA：方差分析；SC：皮肤电导；RMS：均方根；AVHR：平均心率；HR：心率；LF/HF：低频段与高频段的比值；PSD：功率谱密度；MF：中频；HRV：心率变异性；BPNN：反向传播神经网络 邓迪压力状态问卷；ANOVA：方差分析；SC：皮肤电导；RMS：均方根；AVHR：平均心率；HR：心率；LF/HF：低频段与高频段的比值；PSD：功率谱密度；MF：中频；HRV：心率变异性；BPNN：反向传播神经网络 邓迪压力状态问卷；ANOVA：方差分析；SC：皮肤电导；RMS：均方根；AVHR：平均心率；HR：心率；LF/HF：低频段与高频段的比值；PSD：功率谱密度；MF：中频；HRV：心率变异性；BPNN：反向传播神经网络