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

   https://doi.org/10.3390/s19173766  

   Akbar, F.; Mark, G.; Pavlidis, I.; Gutierrez-Osuna, R. (August 2019, Sensors)

   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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2. Semi-Supervised Learning for Wearable-based Momentary Stress Detection in the Wild

   https://doi.org/10.1145/3596246  

   Yu, Han; Sano, Akane (June 2023, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Physiological and behavioral data collected from wearable or mobile sensors have been used to estimate self-reported stress levels. Since stress annotation usually relies on self-reports during the study, a limited amount of labeled data can be an obstacle to developing accurate and generalized stress-predicting models. On the other hand, the sensors can continuously capture signals without annotations. This work investigates leveraging unlabeled wearable sensor data for stress detection in the wild. We propose a two-stage semi-supervised learning framework that leverages wearable sensor data to help with stress detection. The proposed structure consists of an auto-encoder pre-training method for learning information from unlabeled data and the consistency regularization approach to enhance the robustness of the model. Besides, we propose a novel active sampling method for selecting unlabeled samples to avoid introducing redundant information to the model. We validate these methods using two datasets with physiological signals and stress labels collected in the wild, as well as four human activity recognition (HAR) datasets to evaluate the generality of the proposed method. Our approach demonstrated competitive results for stress detection, improving stress classification performance by approximately 7% to 10% on the stress detection datasets compared to the baseline supervised learning models. Furthermore, the ablation study we conducted for the HAR tasks supported the effectiveness of our methods. Our approach showed comparable performance to state-of-the-art semi-supervised learning methods for both stress detection and HAR tasks. 

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3. Design considerations of a wearable electronic-skin for mental health and wellness: balancing bio signals and human factors

   https://doi.org/10.1101/2021.01.20.427496  

   Yasser Khan, Matthew L. (January 2021, bioRxiv)

   Chronic stress has been associated with a variety of pathophysiological risks including developing mental illness. Conversely, appropriate stress management, can be used to foster mental wellness proactively. Yet, there is no existing method that accurately and objectively monitors stress. With recent advances in electronic-skin (e-skin) and wearable technologies, it is possible to design devices that continuously measure physiological parameters linked to chronic stress and other mental health and wellness conditions. However, the design approach should be different from conventional wearables due to considerations like signal-to-noise ratio and the risk of stigmatization. Here, we present a multi-part study that combines user-centered design with engineering-centered data collection to inform future design efforts. To assess human factors, we conducted an n=24 participant design probe study that examined perceptions of an e-skin for mental health and wellness as well as preferred wear locations. We complement this with an n=10 and n=16 participant data collection study to measure physiological signals at several potential wear locations. By balancing human factors and biosignals, we conclude that the upper arm and forearm are optimal wear locations. 

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4. Wireless batteryless soft sensors for ambulatory cardiovascular health monitoring

   https://doi.org/10.20517/ss.2023.17  

   Guess, Matthew; Soltis, Ira; Rigo, Bruno; Zavanelli, Nathan; Kapasi, Sara; Kim, Hyeonseok; Yeo, Woon-Hong (January 2023, Soft Science)

   Seismocardiography (SCG) is the measure of local vibrations in the chest due to heartbeats. Typically, SCG signals are measured using rigid integrated circuit (IC) accelerometers and bulky electronics. However, as alternatives, recent studies of emerging flexible sensors show promise. Here, we introduce the development of wireless soft capacitive sensors that require no battery or rigid IC components for measuring SCG signals for cardiovascular health monitoring. Both the capacitive and inductive components of the circuit are patterned with laser micromachining of a polyimide-coated copper and are encapsulated with an elastomer. The wearable soft sensor can detect small strain changes on the skin, which is wirelessly measured by examining the power reflected from the antenna at a stimulating frequency. The performance of the device is verified by comparing the fiducial points to SCG measured by a commercial accelerometer and electromyograms from a commercial electrode. Overall, the human subject study demonstrates that the fiducial points are consistent with data from commercial devices, showing the potential of the ultrathin soft sensors for ambulatory cardiovascular monitoring without bulky electronics and rigid components. 

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5. Optimizing Sensor Locations for Electrodermal Activity Monitoring Using a Wearable Belt System

   https://doi.org/10.3390/jsan14020031  

   McNaboe, Riley Q; Kong, Youngsun; Henderson, Wendy A; Cong, Xiaomei; Li, Aolan; Seo, Min-Hee; Chen, Ming-Hui; Feng, Bin; Posada-Quintero, Hugo F (April 2025, Journal of Sensor and Actuator Networks)

   Wearable devices for continuous health monitoring in humans are constantly evolving, yet the signal quality may be improved by optimizing electrode placement. While the commonly used locations to measure electrodermal activity (EDA) are at the fingers or the wrist, alternative locations, such as the torso, need to be considered when applying an integrated multimodal approach of concurrently recording multiple bio-signals, such as the monitoring of visceral pain symptoms like those related to irritable bowel syndrome (IBS). This study aims to quantitatively determine the EDA signal quality at four torso locations (mid-chest, upper abdomen, lower back, and mid-back) in comparison to EDA signals recorded from the fingers. Concurrent EDA signals from five body locations were collected from twenty healthy participants as they completed a Stroop Task and a Cold Pressor task that elicited salient autonomic responses. Mean skin conductance (meanSCL), non-specific skin conductance responses (NS.SCRs), and sympathetic response (TVSymp) were derived from the torso EDA signals and compared with signals from the fingers. Notably, TVSymp recorded from the mid-chest location showed significant changes between baseline and Stroop phase, consistent with the TVSymp recorded from the fingers. A high correlation (0.77–0.83) was also identified between TVSymp recorded from the fingers and three torso locations: mid-chest, upper abdomen, and lower back locations. While the fingertips remain the optimal site for EDA measurement, the mid-chest exhibited the strongest potential as an alternative recording site, with the upper abdomen and lower back also demonstrating promising results. These findings suggest that torso-based EDA measurements have the potential to provide reliable measurement of sympathetic neural activities and may be incorporated into a wearable belt system for multimodal monitoring. 

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