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

Abstract

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.

A Usability Study of Physiological Measurement in School Using Wearable Sensors

“…Most of HR samples fall between resting-state HR of European adolescents (77.4 and 81.1 bpm for boys and girls, respectively [ 35 ]) and HR during moderate movement (97 and 101 bpm for boys and girls, respectively calculated by averaging between standing rest-state HR and the lowest exercising HR [ 36 ]). The EDA of participants (mean = 15.98, SD = 7.21 S) was considerably higher than found using wearable EDA devices recording at the wrist with dry electrodes [ 7 , 37 ] which can be explained by the higher density of sweat glands at the palm [ 25 ]. The EDA panel Figure 2 shows a conspicuous peak on the left reflecting a high number of EDA signals lower than 0.5 S. The is likely caused by the loss of attachment between electrode and palmar skin.…”

“…Specifically, fingers and palm have a higher density of sweat glands compared to wrist causing differences in level of SCL, SCR, range, and sensitivity in response to arousing events [ 25 ]. EDA levels recorded from the wrist-type sensor were found to be typically around the lower bound of valid EDA range (0.05–60 S) [ 7 ], making it hard to detect small EDA responses [ 59 ] and resulting in limited performance in detecting short mild stressors [ 60 ], especially when comparing with the performance of palm-based EDA device [ 61 ]. For a rule-based approach, it is necessary to establish specific criteria that match expert ratings [ 41 ], which may not generalize across signals captured from different devices, contexts, or study goals.…”

“…Notably, physiological signals are often confounded with factors like these [ 6 ], therefore, threatening the validity of ambulatory assessments. Second, deploying wearable physiological sensors in the field requires nuanced understanding of the cost and benefit involved [ 7 ], device acceptability, usability, and robustness [ 8 ]. Finally, while there is a call for the validation of commercially available wearable physiological sensors by independent parties, only a few of them had been validated.…”

Prediction of state anxiety by machine learning applied to photoplethysmography data

“…Since PPG signal can provide information of PRV ( Jeyhani et al, 2015 ) and blood pressure, which are modulated by autonomic activity, PPG features might be indicative of the psychophysiological status of the subject. Several studies investigated the relationship between PPG and stress, often combining this technique with other technologies acquiring heterogeneous physiological signals ( Cho, Julier & Bianchi-Berthouze, 2019 ; Akbar et al, 2019 ).…”

Design of a Wearable System to Capture Physiological Data to Monitor Surgeons’ Stress During Surgery

“…The current gold standard for evaluating stress is selfreporting; however, this is subjective, affected by memory and emotional expression biases, and do not allow for continuous in-situ monitoring [7]. For an objective and realtime assessment, research efforts have focused on technologies that measure the physiological effects of stress.…”