Evaluation of an Integrated System of Wearable Physiological Sensors for Stress Monitoring in Working Environments by Using Biological Markers

Betti, Stefano; Lova, Raffaele Molino; Rovini, Erika; Acerbi, Giorgia; Santarelli, Luca; Cabiati, Manuela; Ry, Silvia Del; Cavallo, Filippo

doi:10.1109/tbme.2017.2764507

Cited by 117 publications

(58 citation statements)

References 50 publications

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“…The main contribution of our work lies in the breadth of sensor comparisons we used and the context in which they took place. A few other studies in affective computing have conducted sensor comparisons (e.g., [32,70,71]). However, our study is the first to compare thermal imaging and wearable sensors, capturing multiple physiological variables from different parts of the body with different measurement techniques.…”

Section: Discussionmentioning

confidence: 99%

An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work

Akbar

Mark

Pavlidis

et al. 2019

Sensors

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Several unobtrusive sensors have been tested in studies to capture physiological reactions to stress in workplace settings. Lab studies tend to focus on assessing sensors during a specific computer task, while in situ studies tend to offer a generalized view of sensors’ efficacy for workplace stress monitoring, without discriminating different tasks. Given the variation in workplace computer activities, this study investigates the efficacy of unobtrusive sensors for stress measurement across a variety of tasks. We present a comparison of five physiological measurements obtained in a lab experiment, where participants completed six different computer tasks, while we measured their stress levels using a chest-band (ECG, respiration), a wristband (PPG and EDA), and an emerging thermal imaging method (perinasal perspiration). We found that thermal imaging can detect increased stress for most participants across all tasks, while wrist and chest sensors were less generalizable across tasks and participants. We summarize the costs and benefits of each sensor stream, and show how some computer use scenarios present usability and reliability challenges for stress monitoring with certain physiological sensors. We provide recommendations for researchers and system builders for measuring stress with physiological sensors during workplace computer use.

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Section: Discussionmentioning

confidence: 99%

An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work

Akbar

Mark

Pavlidis

et al. 2019

Sensors

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“…To compare our deep learning approach with conventional machine learning approaches, we also developed several machine learning models for use as benchmarks. Here, we selected ECG and RESP features that have been used in many previous studies [11,12,17,18,19].…”

Section: Methodsmentioning

confidence: 99%

“…Numerous studies have proposed machine learning approaches for recognizing mental stress based on various types of physiological signals [8,9,11,16]. Of these signals, ECGs and photoplethysmograms (PPGs) have been used to extract handcrafted features related to heart activity, such as the HR and HR variability (HRV).…”

Section: Related Workmentioning

confidence: 99%

“…Betti et al [11] proposed a wearable physiological sensor system for monitoring stress. They conducted Maastricht Acute Stress Tests and collected multiple physiological signals, including ECGs, EDA, and EEGs.…”

Section: Related Workmentioning

confidence: 99%

“…Similar to the importance of the developments in monitoring devices, developing algorithms to analyze the collected data and accurately recognize the occurrences of stress is also crucial. Several machine learning models have been proposed to recognize stress based on multiple physiological signals [8,9,10,11,12]. Although these models have demonstrated the feasibility of recognizing stress, they have one serious limitation, namely that machine learning approaches require us to extract well-defined, handcrafted features and find the best way to combine them, both very challenging tasks [13].…”

Section: Introductionmentioning

confidence: 99%

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Deep ECG-Respiration Network (DeepER Net) for Recognizing Mental Stress

Seo

Kim

et al. 2019

Sensors

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Unmanaged long-term mental stress in the workplace can lead to serious health problems and reduced productivity. To prevent this, it is important to recognize and relieve mental stress in a timely manner. Here, we propose a novel stress detection algorithm based on end-to-end deep learning using multiple physiological signals, such as electrocardiogram (ECG) and respiration (RESP) signal. To mimic workplace stress in our experiments, we used Stroop and math tasks as stressors, with each stressor being followed by a relaxation task. Herein, we recruited 18 subjects and measured both ECG and RESP signals using Zephyr BioHarness 3.0. After five-fold cross validation, the proposed network performed well, with an average accuracy of 83.9%, an average F1 score of 0.81, and an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.92, demonstrating its superiority over conventional machine learning models. Furthermore, by visualizing the activation of the trained network’s neurons, we found that they were activated by specific ECG and RESP patterns. In conclusion, we successfully validated the feasibility of end-to-end deep learning using multiple physiological signals for recognition of mental stress in the workplace. We believe that this is a promising approach that will help to improve the quality of life of people suffering from long-term work-related mental stress.

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Brain and Behavior: Blending of Human and Artificial Minds Toward Stress Recognition and Intervention in Organizational Well‐Being

Solanky¹,

Gupta²

2022

Impact of Artificial Intelligence on Organizational Transformation

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Evaluation of an Integrated System of Wearable Physiological Sensors for Stress Monitoring in Working Environments by Using Biological Markers

Abstract: The results of this pilot study may be useful in designing portable and remote control systems, such as medical devices used to turn on interventions and prevent stress consequences.

Cited by 117 publications

References 50 publications

An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work

An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work

Deep ECG-Respiration Network (DeepER Net) for Recognizing Mental Stress

Brain and Behavior: Blending of Human and Artificial Minds Toward Stress Recognition and Intervention in Organizational Well‐Being

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