Mental Stress Assessment in the Workplace: A Review

Ghinwa Masri, Fares Al-Shargie, Usman Tariq, Fadwa Almughairbi, Fabio Babiloni, Hasan Al-Nashash

Research output: Contribution to journalArticlepeer-review


Workers with demanding jobs are at risk of experiencing mental stress, leading to decreased performance, mental illness, and disrupted sleep. To detect elevated stress levels in the workplace, studies have explored stress measurement from physiological, psychological, and behavioral perspectives. This paper reviews the assessment methods and strategies for mitigating mental stress in the workplace and provides recommendations for early detection and mitigation of mental stress. Among the modalities, Electroencephalography (EEG), Electrocardiography (ECG) and Galvanic Skin Response (GSR) were found to be the most used in assessing mental stress in the workplace. Nevertheless, these modalities are sensitive to motion artifacts and are difficult to be integrated into real work environments. To further improve stress level assessment in the workplace, multimodality integration with a reduced number of sensors such as EEG, GSR and Functional near infrared spectroscopy (fNIRS) can be utilized. This would lead to developing strategies for stress management in real-time. Furthermore, combining EEG with fNIRS would improve source localization of mental stress. To mitigate stress, we recommend developing a closed loop system that incorporates brain data acquisition systems and machine learning with physical stimulations such as audio Binaural Beats Stimulation and/or Transcranial Electric Stimulation.

Original languageEnglish
Pages (from-to)1-20
Number of pages20
JournalIEEE Transactions on Affective Computing
Publication statusAccepted/In press - 2023


  • Electrocardiography
  • Electroencephalography
  • Employment
  • Human factors
  • Mental Stress
  • Occupational stress
  • Stress
  • Stress measurement
  • behavioral data
  • bio-signal
  • machine learning
  • stress mitigation
  • workplace

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction


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