Machine learning, deep learning and data preprocessing techniques for detection, prediction, and monitoring of stress and stress-related mental disorders: a scoping …

M Razavi, S Ziyadidegan, R Jahromi… - arXiv preprint arXiv …, 2023 - arxiv.org
This comprehensive review systematically evaluates Machine Learning (ML) methodologies
employed in the detection, prediction, and analysis of mental stress and its consequent …

On the domain adaptation and generalization of pretrained language models: A survey

X Guo, H Yu - arXiv preprint arXiv:2211.03154, 2022 - arxiv.org
Recent advances in NLP are brought by a range of large-scale pretrained language models
(PLMs). These PLMs have brought significant performance gains for a range of NLP tasks …

[HTML][HTML] Sense and learn: Self-supervision for omnipresent sensors

A Saeed, V Ungureanu, B Gfeller - Machine Learning with Applications, 2021 - Elsevier
Learning general-purpose representations from multisensor data produced by the
omnipresent sensing systems (or IoT in general) has numerous applications in diverse use …

[HTML][HTML] Machine Learning, Deep Learning, and Data Preprocessing Techniques for Detecting, Predicting, and Monitoring Stress and Stress-Related Mental Disorders …

M Razavi, S Ziyadidegan, A Mahmoudzadeh… - JMIR Mental …, 2024 - mental.jmir.org
Background Mental stress and its consequent mental health disorders (MDs) constitute a
significant public health issue. With the advent of machine learning (ML), there is potential to …

DeStress: deep learning for unsupervised identification of mental stress in firefighters from heart-rate variability (HRV) data

A Oskooei, SM Chau, J Weiss, A Sridhar… - Explainable AI in …, 2021 - Springer
In this work we perform a study of various unsupervised methods to identify mental stress in
firefighter trainees based on unlabeled heart rate variability data. We collect RR interval time …

Psychophysiological evaluation of seafarers to improve training in maritime virtual simulator

Y Liu, Z Lan, J Cui, G Krishnan, O Sourina… - Advanced Engineering …, 2020 - Elsevier
Over the years, safety in maritime industries has been reinforced by many state-of-the-art
technologies. However, the accident rate hasn't dropped significantly with the advanced …

Sifdrivenet: Speed and image fusion for driving behavior classification network

Y Gong, J Lu, W Liu, Z Li, X Jiang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Driving behavior classification is an important direction in the field of social transportation
systems and advanced driving assistance system (ADAS), which has attracted more and …

Leverage social media for personalized stress detection

X Wang, H Zhang, L Cao, L Feng - Proceedings of the 28th ACM …, 2020 - dl.acm.org
Timely detection of stress is desirable to address the increasingly serious stress problem.
Thanks to the rich linguistic expressions and complete historical records on social media …

Importance of testing with independent subjects and contexts for machine-learning models to monitor construction workers' psychophysiological responses

G Lee, SH Lee - Journal of Construction Engineering and …, 2022 - ascelibrary.org
Because workers' abnormal psychophysiological responses (eg, high levels of stress and
fatigue) are directly or indirectly linked to disorders and accidents at construction sites …

Real-time stress detection based on artificial intelligence for people with an intellectual disability

S de Vries, F van Oost, H Smaling, N de Knegt… - 2024 - Taylor & Francis
People with severe intellectual disabilities (ID) could have difficulty expressing their stress
which may complicate timely responses from caregivers. The present study proposes an …