Mental fatigue detection using a wearable commodity device and machine learning

Goumopoulos, Christos; Potha, Nektaria

doi:10.1007/s12652-021-03674-z

Cited by 24 publications

(12 citation statements)

References 48 publications

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“…In line with the notion that the predictive power depends on the time and length of ECG recording, the best performance was achieved by the SVM classifier trained on task-related HRV data calculated for a 5-min time window. The AUC score of 0.84 produced by this model indicates high efficacy and is comparable to the results of previous studies 30 , 31 . The good performance could be explained by several factors, such as having a large sample size or using recursive feature elimination for feature selection, which has not been used in previous studies.…”

Section: Discussionsupporting

confidence: 86%

“…Compared with previous studies that used only a single task for fatigue induction, the fatigue classification algorithms in the present study performed at a similar level 23,[29][30][31] ; the AUC scores and balanced accuracies in this study ranged from 0.685 to 0.841 and from 68 to 76%, respectively. The predictive power mainly depended on two factors: (1) the time of ECG recording, and (2) the time window used for HRV calculation.…”

Section: Discussionmentioning

confidence: 58%

“…Fourth, we trained the models using cross-validation on the training data set, but the final evaluation was based on a previously unseen data set so we could observe how well the models generalise to new data. This analytic approach differs from that of the previous studies 23 , 29 – 31 , which used cross-validation only and did not test the models on unseen holdout data.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning studies have shown that models trained exclusively on HRV are capable of effectively detecting fatigue with the best accuracy scores ranging between 75 and 91% 23,[29][30][31] . Laurent et al (2013), for example, showed that the support vector machine (SVM) algorithm trained on HRV data recorded during the performance of a fatiguing switching task (i.e., algorithm trained on task related HRV data) was able to detect fatigue with an accuracy up to approximately 80%.…”

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confidence: 99%

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Generalisable machine learning models trained on heart rate variability data to predict mental fatigue

Matuz

Linden

Darnai

et al. 2022

Sci Rep

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A prolonged period of cognitive performance often leads to mental fatigue, a psychobiological state that increases the risk of injury and accidents. Previous studies have trained machine learning algorithms on Heart Rate Variability (HRV) data to detect fatigue in order to prevent its consequences. However, the results of these studies cannot be generalised because of various methodological issues including the use of only one type of cognitive task to induce fatigue which makes any predictions task-specific. In this study, we combined the datasets of three experiments each of which applied different cognitive tasks for fatigue induction and trained algorithms that detect fatigue and predict its severity. We also tested different time window lengths and compared algorithms trained on resting and task related data. We found that classification performance was best when the support vector classifier was trained on task related HRV calculated for a 5-min time window (AUC = 0.843, accuracy = 0.761). For the prediction of fatigue severity, CatBoost regression showed the best performance when trained on 3-min HRV data and self-reported measures (R2 = 0.248, RMSE = 17.058). These results indicate that both the detection and prediction of fatigue based on HRV are effective when machine learning models are trained on heterogeneous, multi-task datasets.

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Section: Discussionsupporting

confidence: 86%

Section: Discussionmentioning

confidence: 58%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Generalisable machine learning models trained on heart rate variability data to predict mental fatigue

Matuz

Linden

Darnai

et al. 2022

Sci Rep

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“…Betti et al [12] utilized wearable devices to collect adult EEG, electrocardiography (ECG) and electrodermal activity (EDA) and then analysed this data using the support vector machine (SVM) to detect individual stress levels. Also, Goumopoulos et al [13] suggested a method to detect mental fatigue. The authors utilized smart wearables to collect person's HRV and then utilize machine learning to predict mental fatigue.…”

Section: State-of-the-art Of Ai and Emotions Recognitionmentioning

confidence: 99%

Emotions in Mental Healthcare and Psychological Interventions: Towards an Inventive Emotions Recognition Framework Using AI

Atlam

Shafik

Kurugöllü

et al. 2022

Advances in Transdisciplinary Engineering

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One of the major impacts of COVID-19 in the nations is mental health issues. Constant mental health issues can cause disorders, as well as mortality. The growing demand for mental healthcare treatment and limited healthcare resources across the world has shown the need for an inventive framework solution. Artificial Intelligence (AI), Big Data Science, 5G, and Information Communication Technology (ICT) have proven to be able to bring many great improvements and could be the potential way forward to develop such a framework. AI could be a very effective tool to help the healthcare sector to provide more efficient services to patients with mental health issues through their emotions. This paper presents the initial overview and outcomes of the ongoing research programme to develop a proactive multimodal emotion AI recognition framework that detects emotion from various input data sources for early detection of mental health illnesses, as well as provides the required psychological interventions effectively and promptly when required. The data will be collected from various smart wearables and ad-hoc devices, facial expressions, and speech signals. Then, these data will be interpreted using AI into emotions. These emotions will be utilised using AI-based psychological system, which will provide immediate and customized interventions, as well as transmit critical data to the healthcare provider’s central database system for monitoring and supplying the required treatments.

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Applications of Industry 4.0 Technology for Monitoring of Human Errors Using Internet of Things

Noman,

Alqahtani

2024

Lecture Notes in Mechanical Engineering

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Mental fatigue detection using a wearable commodity device and machine learning

Cited by 24 publications

References 48 publications

Generalisable machine learning models trained on heart rate variability data to predict mental fatigue

Generalisable machine learning models trained on heart rate variability data to predict mental fatigue

Emotions in Mental Healthcare and Psychological Interventions: Towards an Inventive Emotions Recognition Framework Using AI

Applications of Industry 4.0 Technology for Monitoring of Human Errors Using Internet of Things

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