Stress detection using deep neural networks

Li, Russell A.; Liu, Zhandong

doi:10.1186/s12911-020-01299-4

Cited by 88 publications

(29 citation statements)

References 25 publications

(32 reference statements)

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“…When those data were combined with global positioning system (GPS) and Wi-Fi information, these features used allowed to detect a change of behavior in about 86% of the participants during stressful times [ 26 ]. Li et al [ 27 ] implemented a deep neural network model to perform two classification tasks. A binary stress detection and a 3-class emotion classification using physiological signals collected from wrist-worn and chest-worn sensors.…”

Section: Introductionmentioning

confidence: 99%

Medically-oriented design for explainable AI for stress prediction from physiological measurements

Jaber

Hajj

Maalouf

et al. 2022

BMC Med Inform Decis Mak

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Background In the last decade, a lot of attention has been given to develop artificial intelligence (AI) solutions for mental health using machine learning. To build trust in AI applications, it is crucial for AI systems to provide for practitioners and patients the reasons behind the AI decisions. This is referred to as Explainable AI. While there has been significant progress in developing stress prediction models, little work has been done to develop explainable AI for mental health. Methods In this work, we address this gap by designing an explanatory AI report for stress prediction from wearable sensors. Because medical practitioners and patients are likely to be familiar with blood test reports, we modeled the look and feel of the explanatory AI on those of a standard blood test report. The report includes stress prediction and the physiological signals related to stressful episodes. In addition to the new design for explaining AI in mental health, the work includes the following contributions: Methods to automatically generate different components of the report, an approach for evaluating and validating the accuracies of the explanations, and a collection of ground truth of relationships between physiological measurements and stress prediction. Results Test results showed that the explanations were consistent with ground truth. The reference intervals for stress versus non-stress were quite distinctive with little variation. In addition to the quantitative evaluations, a qualitative survey, conducted by three expert psychiatrists confirmed the usefulness of the explanation report in understanding the different aspects of the AI system. Conclusion In this work, we have provided a new design for explainable AI used in stress prediction based on physiological measurements. Based on the report, users and medical practitioners can determine what biological features have the most impact on the prediction of stress in addition to any health-related abnormalities. The effectiveness of the explainable AI report was evaluated using a quantitative and a qualitative assessment. The stress prediction accuracy was shown to be comparable to state-of-the-art. The contributions of each physiological signal to the stress prediction was shown to correlate with ground truth. In addition to these quantitative evaluations, a qualitative survey with psychiatrists confirmed the confidence and effectiveness of the explanation report in the stress made by the AI system. Future work includes the addition of more explanatory features related to other emotional states of the patient, such as sadness, relaxation, anxiousness, or happiness.

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Section: Introductionmentioning

confidence: 99%

Medically-oriented design for explainable AI for stress prediction from physiological measurements

Jaber

Hajj

Maalouf

et al. 2022

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…In the past several decades, neurophysiological signal have played an important role in mental workload detection because of its objectivity and stability[ 9 ]. One of the major topics to be investigated in this field is electroencephalogram (EEG)-based mental workload detection method.…”

Section: Introductionmentioning

confidence: 99%

Optimized electroencephalogram and functional near-infrared spectroscopy-based mental workload detection method for practical applications

et al. 2022

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Background Mental workload is a critical consideration in complex man–machine systems design. Among various mental workload detection techniques, multimodal detection techniques integrating electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals have attracted considerable attention. However, existing EEG–fNIRS-based mental workload detection methods have certain defects, such as complex signal acquisition channels and low detection accuracy, which restrict their practical application. Methods The signal acquisition configuration was optimized by analyzing the feature importance in mental workload recognition model and a more accurate and convenient EEG–fNIRS-based mental workload detection method was constructed. A classical Multi-Task Attribute Battery (MATB) task was conducted with 20 participating volunteers. Subjective scale data, 64-channel EEG data, and two-channel fNIRS data were collected. Results A higher number of EEG channels correspond to higher detection accuracy. However, there is no obvious improvement in accuracy once the number of EEG channels reaches 26, with a four-level mental workload detection accuracy of 76.25 ± 5.21%. Partial results of physiological analysis verify the results of previous studies, such as that the θ power of EEG and concentration of O2Hb in the prefrontal region increase while the concentration of HHb decreases with task difficulty. It was further observed, for the first time, that the energy of each band of EEG signals was significantly different in the occipital lobe region, and the power of $$\beta_{1}$$ β 1 and $$\beta_{2}$$ β 2 bands in the occipital region increased significantly with task difficulty. The changing range and the mean amplitude of O2Hb in high-difficulty tasks were significantly higher compared with those in low-difficulty tasks. Conclusions The channel configuration of EEG–fNIRS-based mental workload detection was optimized to 26 EEG channels and two frontal fNIRS channels. A four-level mental workload detection accuracy of 76.25 ± 5.21% was obtained, which is higher than previously reported results. The proposed configuration can promote the application of mental workload detection technology in military, driving, and other complex human–computer interaction systems.

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“…Reference [21] used only pixels and disease labels as the input of CNNs to automatically classify skin lesions, with a level of competence almost equal to dermatologists. In Reference [22], the designed neural network was used to complete binary stress detection and three types of emotion classification. In Reference [23], the contextual deep CNN predicted the corresponding label of each pixel vector to complete hyper-spectral image classification.…”

Section: Introductionmentioning

confidence: 99%

CNN-Based Hybrid Optimization for Anomaly Detection of Rudder System

Wang¹,

Yang

Guo

et al. 2021

IEEE Access

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In this study, an automatic test platform suitable for steering gears was established, which can test four sets of rudder systems separately. In addition, we propose an anomaly detection method based on deep learning technology to complete the automated multi-fault classification of the steering gear test. This paper combines the particle swarm optimization algorithm and the grey wolf optimization algorithm to optimize the convolutional neural networks (HPSOGWO-CNN). The proposed HPSOGWO-CNN model is constructed in two stages to realize the efficient and high-accuracy anomaly detection of the rudder system. In the first stage, through 10-fold cross validation, the optimal number of search agents of the HPSOGWO algorithm is obtained, and the performance is compared with GWO and PSO algorithms respectively. The results demonstrate that HPSOGWO algorithm is an excellent technique for automatic selection of hyperparameters. In the second stage, the designed HPSOGWO algorithm is used to fine-tune the hyperparameters of CNN, and a highly matched model for anomaly detection of rudder system test parameters was finally obtained. The experimental results show that the accuracy of this method is 99.846%, the precision is 99.748%, the recall is 99.498%, the F-score is 99.618%, and Kappa reaches 0.99565. CNNbased hybrid optimization for anomaly detection of rudder system, is advanced in comparison to KNN, SVM, BP, CNN, PSO-CNN, GWO-CNN, MGWO-CNN, WdGWO-CNN, RW-GWO-CNN models, in terms of accuracy, precision, recall, F-score, and kappa, respectively. Moreover, it is not affected by the imbalance samples, and can achieve accurate classification for small training samples.

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Stress detection using deep neural networks

Cited by 88 publications

References 25 publications

Medically-oriented design for explainable AI for stress prediction from physiological measurements

Medically-oriented design for explainable AI for stress prediction from physiological measurements

Optimized electroencephalogram and functional near-infrared spectroscopy-based mental workload detection method for practical applications

CNN-Based Hybrid Optimization for Anomaly Detection of Rudder System

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