mlsanityData to 7 Oct 2026

Human Stress Assessment: A Comprehensive Review of Methods Using Wearable Sensors and Non-wearable Techniques

arXiv 2202.03033 ↗First posted 7 Feb 2022cs.HC cs.AIcited by 2

Not on the map. Why?

Abstract (arXiv)

This paper presents a comprehensive review of methods covering significant subjective and objective human stress detection techniques available in the literature. The methods for measuring human stress responses could include subjective questionnaires (developed by psychologists) and objective markers observed using data from wearable and non-wearable sensors. In particular, wearable sensor-based methods commonly use data from electroencephalography, electrocardiogram, galvanic skin response, electromyography, electrodermal activity, heart rate, heart rate variability, and photoplethysmography both individually and in multimodal fusion strategies. Whereas, methods based on non-wearable sensors include strategies such as analyzing pupil dilation and speech, smartphone data, eye movement, body posture, and thermal imaging. Whenever a stressful situation is encountered by an individual, physiological, physical, or behavioral change is induced which help in coping with the challenge at hand. A wide range of studies has attempted to establish a relationship between these stressful situations and the response of human beings by using different kinds of psychological, physiological, physical, and behavioral measures. Inspired by the lack of availability of a definitive verdict about the relationship of human stress with these different kinds of markers, a detailed survey about human stress detection methods is conducted in this paper. In particular, we explore how stress detection methods can benefit from artificial intelligence utilizing relevant data from various sources. This review will prove to be a reference document that would provide guidelines for future research enabling effective detection of human stress conditions.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 7 of its 526 references that are in the map. First posted on arXiv 7 Feb 2022.

Builds on

7 of 526 references.

References · 7

  1. 2015Automatic Stress Detection in Working Environments from Smartphones' Accelerometer Data: A First Stepcited by 12
  2. 2018Instant Automated Inference of Perceived Mental Stress through Smartphone PPG and Thermal Imagingcited by 12
  3. 2017DeepBreath: Deep Learning of Breathing Patterns for Automatic Stress Recognition using Low-Cost Thermal Imaging in Unconstrained Settingscited by 9
  4. 2014Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traitscited by 5
  5. 2019DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-rate Variability (HRV) Datacited by 3
  6. 2019Classification of Perceived Human Stress using Physiological Signalscited by 2
  7. 2017ThermSense: Smartphone-based Breathing Sensing Platform using Noncontact Low-Cost Thermal Cameracited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.