Stress is an inherent aspect of daily life and the use of consumer wearables for stress monitoring has grown significantly. However, existing stress monitoring technologies frequently detect physiological arousal triggered by routine demands, but face challenges in distinguishing between negative stress, such as threat, and positive stress, such as challenge. This differentiation is crucial in minimizing false alarms in stress management notifications and enabling personalized interventions specifically linked to negative stress arousal. To tackle this problem, we leverage advanced biomarkers by detecting ballistocardiogram (BCG) responses through earbud motion sensors and integrating them with biomarkers derived from earbud photoplethysmography (PPG) sensors. A meticulously designed study was conducted to elicit both threat and challenge responses in a controlled laboratory environment. Participants wore custom-designed earbuds for synchronized PPG and accelerometer (BCG) data collection, along with a reference biosig-nal acquisition system. We developed a biomarker extraction pipeline utilizing state-of-the-art BCG and PPG signal processing algorithms to filter low-quality signals and accurately extract advanced physiological biomarkers that capture information related to both challenge and threat responses. Our findings reveal that algorithms applied to earbud data can effectively extract stress biomarkers for arousal detection and differentiate between threat and challenge arousal, achieving an F1-score of up to 81%.

The earbud-based ballistocardiogram (BCG) assessment holds significant promise for monitoring diverse physiological signals, including stress, cardiac activity, and blood pressure. However, unlike traditional methods that measure the force component along the head-to-foot axis for enhanced BCG signal quality, ear-worn devices are prone to orientation misalignment, leading to significant variations in BCG morphology. To address this challenge, we propose two novel algorithms: one that employs sensor-to-body-segment calibration and another that applies a calibration-free, physiologically informed axis fusion method to enhance earbud-based BCG signal assessment. We evaluate the performance of these approaches against existing methods, focusing on heart rate variability (HRV) estimation and morphological feature extraction. Through a comprehensive investigation, we aim to identify optimal strategies for obtaining high-quality BCG signals using ear-worn devices.

This study evaluates the usability of wearable head ballistocardiography (BCG) in providing accurate pulse transit time (PTT) measurements during blood pressure (BP) interventions. Head BCG is a new technique enabling measurement of proximal aortic blood ejection from sensors placed at distal sites, which envisions PTT measurement from single integrated device for cuff-less BP estimation. However, due to its low signal-to-noise ratio and sensitivity to motion artifacts, accurate beat selection is crucial to ensure the integrity of PTT calculation. In this paper, using inertial measurement unit (IMU) sensors integrated in a prototype earbud, we investigate whether the wearable head BCG signal is aligned with the ground-truth proximal reference acquired from the synchronously-recorded impedance cardiography (ICG) signal, to assess the usability of head BCG as the proximal indicator for PTT measurement at different stages of BP intervention. Wearable BCG signals showed highest reliability during resting states, with 63% of detected j-peaks aligned with ground-truth ICG signals. Beat selection via removal of IBI outliers improves the ratio of reliable peaks, at rest (68%) and during exercise (63% at intervention and 52% at plateau). Other methods, such as template matching or rejecting amplitude outliers, only improve the ratio at rest. Overall, this study reveals characteristics of distortions in the head BCG signal during intervention, as a first step toward robust solutions for PTT-based BP tracking on integrated wearable devices.

Cardiac Output (CO) is a critical indicator of health, offering insights into cardiac dysfunction, acute stress responses, and cognitive decline. Traditional CO monitoring methods, like impedance cardiography, are invasive and impractical for daily use, leading to a gap in continuous, non-invasive monitoring. Although recent advancements explored wearables on heart rate monitoring, these approaches face challenges in accurately estimating CO due to the indirect nature of the signals. To address these challenges, we introduce EarCO, a non-invasive multimodal CO monitoring system with Photoplethysmography and Ballistocardiogram signals on commodity earbuds. A novel feature fusion method is proposed to integrate raw signals and prior knowledge from both modalities, improving the system’s interpretability and accuracy. EarCO achieves an error of 1.080 L/min in the leave-one-subject-out settings with 62 subjects, making cardiovascular health monitoring accessible and practical for daily use.

Slow-paced deep breathing exercises have many health benefits, including stress management, lowering blood pressure, pain management, and controlling pulmonary conditions. While biofeedback can significantly improve the efficacy of breathing exercises, existing approaches support limited biomarkers, such as breathing rate, for specific breathing exercises (e.g., equal-phase breathing) in particular conditions without considering the breath-holding phase or variation in device orientation. Therefore, there needs to be a more convenient and robust approach that can generate and deliver comprehensive digital breathing biomarkers to facilitate biofeedback for various types of breathing exercises. In this article, we present a system with lightweight algorithms to passively track mindful breathing in real-time using lower-power earbud motion sensors to extract fine-grained comprehensive breathing biomarkers for generating biofeedback on users’ breathing exercises. We utilize the earbud’s motion sensor data to detect nonbreathing head motion and develop an extensive set of breathing markers, including breathing phases, breathing depth, breathing rate, breathing symmetry, and breath-holding. Such a comprehensive set of biomarkers can enable engaging user experience and effective mindful breathing exercises toward better stress management and overall mental well-being. Moreover, we develop a physiologically informed, novel earbud orientation handling algorithm that makes our biomarkers more resilient to ear canal shape and size. Finally, we showcase potential use-cases based on the breathing biomarkers derived from our algorithms to provide biofeedback on user’s overall breathing performance.

Core body temperature (CBT) is one of the critical yet under-explored phenomena in the context of stress detection. Several CBT measurement methods exist, but they are often limited in continuous CBT monitoring. Furthermore, how continuous CBT can be used to model acute stress is little explored. We address these challenges by conducting an in-lab controlled study with 97 participants who participated in baseline and stress-inducing tasks while wearing prototype earbuds capable of collecting CBT. We found that accounting for changes from individual baselines in CBT results is acute stress detection with 94.88% accuracy and 94.4% F1-score, which is 29.31% and 26.07% higher in terms of accuracy and F1-score, respectively, compared to generalized features.

Workplace stress has been increasing in recent decades and has worsened by the unique demands imposed by COVID-19 and the new remote/hybrid work settings. High-stress working conditions can be detrimental to the health and wellness of workers and can lead to significant business costs in terms of productivity loss and medical expenses. An essential step toward managing stress involves finding comfortable ways to sense workers and recognizing stress as soon as it happens. This work explores the potential value of using pervasive sensors such as keyboards, webcams, and behavioral data such as calendar and e-mail activity to passively assess individual stress levels of work in real-life. In particular, we collected a large corpus of such data from 46 remote information workers over one month and asked them to self-report their stress levels and other relevant factors several times a day. Analysis of the data demonstrates that passive sensors can effectively detect both triggers and manifestations of workplace stress and that having access to prior data of the worker is critical for developing well-performing stress recognition models. Furthermore, we provide qualitative feedback capturing workers' preferences in workplace stress monitoring.

Contact tracers assist in containing the spread of highly infectious diseases such as COVID-19 by engaging community members who receive a positive test result in order to identify close contacts. Many contact tracers rely on community member’s recall for those identifications, and face limitations such as unreliable memory. To investigate how technology can alleviate this challenge, we developed a visualization tool using de-identified location data sensed from campus WiFi and provided it to contact tracers during mock contact tracing calls. While the visualization allowed contact tracers to find and address inconsistencies due to gaps in community member’s memory, it also introduced inconsistencies such as false-positive and false-negative reports due to imperfect data, and information sharing hesitancy. We suggest design implications for technologies that can better highlight and inform contact tracers of potential areas of inconsistencies, and further present discussion on using imperfect data in decision making.

Workplace stress-reduction interventions have produced mixed results due to engagement and adherence barriers. Leveraging technology to integrate such interventions into the workday may address these barriers and help mitigate the mental, physical, and monetary effects of workplace stress. To inform the design of a workplace stress-reduction intervention system, we conducted a four-week longitudinal study with 86 participants, examining the effects of intervention type and timing on usage, stress reduction impact, and user preferences. We compared three intervention types and two delivery timing conditions: Pre-scheduled (PS) by users and Just-in-time (JIT) prompted by the system-identified user stress-levels. We found JIT participants completed significantly more interventions than PS participants, but post-intervention and study-long stress reduction was not significantly different between conditions. Participants rated low-effort interventions highest, but high-effort interventions reduced the most stress. Participants felt JIT provided accountability but desired partial agency over timing. We present type and timing implications.

Deviant eating behavior such as skipping meals and consuming unhealthy meals has a significant association with mental well-being in college students. However, there is more to what an individual eats. While eating patterns form a critical component of their mental well-being, insights and assessments related to the interplay of eating patterns and mental well-being remain under-explored in theory and practice. To bridge this gap, we use an existing real-time eating detection system that captures context during meals to examine how college students’ eating context associates with their mental well-being, particularly their affect, anxiety, depression, and stress. Our findings suggest that students’ irregularity or skipping meals negatively correlates with their mental well-being, whereas eating with family and friends positively correlates with improved mental well-being. We discuss the implications of our study in designing dietary intervention technologies and guiding student-centric well-being technologies.

Most scholarly discussions around ridesharing applications center on the experiences of the drivers and the riders (passengers), and thus the role of the owners of the cars, if they are different from the drivers, remain understudied. However, in many countries in the Global South, the car owners are often different from the car drivers, and the tensions between them often shape the experience with these ridesharing apps in those countries. In this paper, we address this issue based on our interview-based study in Dhaka, Bangladesh, which incorporates semi-structured interviews of 31 Uber drivers and 10 car owners. From our interviews, we identify the contract models that facilitate the partnership between prospective Uber drivers without a car and car owners seeking to rent their cars for Uber, describe the tensions between these two parties, provide a nuanced cultural portrayal of their negotiation mechanisms, and highlight the reasons for which the driver or the owner leaves Uber. Our analysis reveals how the local adoption of sharing economy amplifies existing inequalities and disrupts the prevailing social dynamics. We also connect our findings to the broader interests of CSCW around work, privacy, power and discuss their implications for design and policy formulations.

Ridesharing services have been viewed as heralding the next generation of mobility and recognized for their potential to provide an alternate and more flexible model of work. These services have also been critiqued for their treatment of employees, low wages, and other concerns. In this paper, we present a qualitative investigation of the introduction of Uber in Dhaka, Bangladesh. Using interview data from drivers and riders, and content analysis of riders' Facebook posts, we highlight how Uber's introduction into Dhaka's existing transportation infrastructure influenced experiences and practices of mobility in the city. Drawing on Iris Marion Young's theory of justice, we demonstrate how the introduction of Uber in Dhaka reinforces existing modes of oppression and introduces new ones, even as it generates room for creative modes of resistance. Finally, we underline algorithms' opacity and veneer of objectivity as a potential source of oppression, call for deepening the postcolonial computing perspective, and make a case for stronger connections between technology interventions and policy.

We present a study of internet use and its forced non-use in Bangladesh. In light of current initiatives by state and industry actors to improve internet access and bridge the 'digital divide' for under-served, under-resourced, and underrepresented communities across the world, we offer a situated, qualitative perspective on what the current state of internet use looks like for select social groups in Bangladesh. We analyze how a state-imposed ban attempted to affect the nonuse of particular web-based services and how the affected populations found or did not find workarounds in response. We also discuss takeaways for researchers as well as industry and state actors studying and working towards more equitable access to the internet in the 'developing' world.