利用上下文洞察和高效建模,通过生理信号和机器学习增强精神疲劳检测

IF 3.3 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Carole-Anne Cos, Alexandre Lambert, Aakash Soni, Haifa Jeridi, Coralie Thieulin, Amine Jaouadi
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引用次数: 0

摘要

本研究提出了一种机器学习建模过程,用于使用三种生理信号:皮肤电活动、心电图和呼吸来检测精神疲劳。它遵循传统的机器学习建模管道,同时强调特征选择过程的重要贡献,从而不仅得到高性能模型,而且得到相关模型。所采用的特征选择过程考虑了特征相关性的统计和上下文方面。通过独立特征与因变量(疲劳状态)之间的方差和相关分析来评估统计相关性。上下文分析是基于从实验设计和特征中得出的见解。此外,采用特征排序和集合转换技术,将生理信号的时间方面纳入基于随机森林、决策树、支持向量机、k近邻和梯度增强的机器学习模型的训练中。使用从可穿戴电子系统(第三方研究)获得的数据集进行评估,其中包括三名受试者进行一系列测试和疲劳阶段的生理数据。3名受试者在3种精神疲劳状态下共进行了18项测试。疲劳评价以主观测量和反应时间测试为主,疲劳诱导采用心算方法。结果表明,在使用随机森林时,对精神疲劳的三个等级进行分类的平均准确率和f1得分达到96%,表现出最高的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing Mental Fatigue Detection through Physiological Signals and Machine Learning Using Contextual Insights and Efficient Modelling
This research presents a machine learning modeling process for detecting mental fatigue using three physiological signals: electrodermal activity, electrocardiogram, and respiration. It follows the conventional machine learning modeling pipeline, while emphasizing the significant contribution of the feature selection process, resulting in, not only a high-performance model, but also a relevant one. The employed feature selection process considers both statistical and contextual aspects of feature relevance. Statistical relevance was assessed through variance and correlation analyses between independent features and the dependent variable (fatigue state). A contextual analysis was based on insights derived from the experimental design and feature characteristics. Additionally, feature sequencing and set conversion techniques were employed to incorporate the temporal aspects of physiological signals into the training of machine learning models based on random forest, decision tree, support vector machine, k-nearest neighbors, and gradient boosting. An evaluation was conducted using a dataset acquired from a wearable electronic system (in third-party research) with physiological data from three subjects undergoing a series of tests and fatigue stages. A total of 18 tests were performed by the 3 subjects in 3 mental fatigue states. Fatigue assessment was based on subjective measures and reaction time tests, and fatigue induction was performed through mental arithmetic operations. The results showed the highest performance when using random forest, achieving an average accuracy and F1-score of 96% in classifying three levels of mental fatigue.
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来源期刊
Journal of Sensor and Actuator Networks
Journal of Sensor and Actuator Networks Physics and Astronomy-Instrumentation
CiteScore
7.90
自引率
2.90%
发文量
70
审稿时长
11 weeks
期刊介绍: Journal of Sensor and Actuator Networks (ISSN 2224-2708) is an international open access journal on the science and technology of sensor and actuator networks. It publishes regular research papers, reviews (including comprehensive reviews on complete sensor and actuator networks), and short communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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