publications | Blue (Georgianna) Lin

2026

Women’s Health Wearables: From Continuous Signals to Actionable Digital Phenotypes Across the Reproductive Lifespan

Rawan AlSaad, Georgianna Lin, Shima Albasha, and 2 more authors

Bioengineering, 2026

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Wearable technologies are reshaping women’s health by extending observation beyond episodic clinical encounters into daily life. Across the reproductive lifespan, they can capture physiological, behavioral, symptom, and functional trajectories that are often missed in routine care. Yet more data do not automatically translate into better care. Clinical value depends on whether multimodal signals can be modeled and interpreted in relation to reproductive biology, temporal change, and meaningful clinical or functional endpoints. In this perspective, we examine how women’s health wearables can move beyond consumer tracking toward validated digital phenotyping across menstruation, fertility, pregnancy, postpartum recovery, and menopause. We propose a four-layer framework spanning data capture, physiological domain mapping, computational phenotyping, and actionable translation. We then apply this framework across key reproductive life stages. Menstrual health and fertility applications illustrate the shift from calendar-based prediction toward physiological, metabolic, and hormone-aware monitoring. Pregnancy and postpartum applications highlight the need for safety-focused validation, maternal-infant risk awareness, and clinician-governed escalation pathways. Menopause and midlife health represent underdeveloped areas where longitudinal digital phenotyping may better capture vasomotor, sleep, mood, fatigue, and functional symptoms. Across these domains, we identify key barriers to translation, including limited hormone-linked validation, inconsistent evidence standards, underrepresentation of diverse populations, privacy risks, algorithmic bias, and weak workflow integration. By organizing wearable-derived signals across reproductive life stages and identifying major translational barriers, this perspective provides a roadmap toward biologically grounded, equitable, and clinically actionable digital phenotyping for women’s health.
A Framework for Longitudinal Health AI Agents

Georgianna Lin, Rencong Jiang, Noémie Elhadad, and 1 more author

Nature Health, 2026

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A longitudinal dataset of physiological, hormonal, metabolic, and self-reported menstrual health data

Georgianna Lin, Jin Yi Li, Kaavya Kalani, and 2 more authors

Scientific Data, 2026

2025

From fertility to overall health: Barriers and catalysts to a holistic menstrual health perspective

Georgianna Lin, Minh Ngoc Le, Pierre-William Lessard, and 3 more authors

ACM Transactions on Computing for Healthcare, 2025
mcPHASES: A Dataset of Physiological, Hormonal, and Self-reported Events and Symptoms for Menstrual Health Tracking with Wearables (version 1.0.0)

Georgianna Lin, Jin Yi Li, Kaavya Kalani, and 2 more authors

2025

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Individuals who menstruate are frequently led to believe that there is a standard menstrual cycle, typically characterized as 28 days in length with predictable and uniform patterns. This framing often emphasizes cycle dates as the only relevant metric, overlooking the broader physiological and emotional fluctuations throughout the cycle driven by complex hormonal interactions. Consequently, when individuals encounter menstrual experiences that do not align with calendar-based metrics, they are often left without adequate frameworks for understanding their menstrual health, which can result in distress or delays in seeking care. Our work advocates for a new definition of menstrual health that encompasses a wider range of physiological signals in order to acknowledge its connection to overall wellbeing, establish realistic expectations for menstruators, and build better health management systems. However, historical stigmatization has led to a dearth of datasets suitable for pursuing these aims. mcPHASES (menstrual cycle Physiological, Hormonal, and Self-Reported Events and Symptoms) is a comprehensive dataset consisting of multimodal physiological, hormonal, and self-reported measures collected to support holistic menstrual health research. Data from 42 Canadian young adult menstruators was collected across two 3-month periods. Participants wore Fitbit Sense smartwatches and Dexcom G6 continuous glucose monitors to measure physiological signals, and they used Mira Plus Starter Kits to track their hormone levels. Additionally, participants self-reported daily experiences like cramps, sleep quality, and stress levels. The dataset contains 23 structured tables organized by signal category so that researchers can examine relationships between physiological signals and hormonal fluctuations, analyze the impacts of lifestyle factors on the menstrual cycle, and develop better algorithms for menstrual cycle prediction. More broadly, mcPHASES supports research in women’s health, digital health technologies, and personalized care by providing unprecedented multimodal data for building a more accurate understanding of menstrual health patterns.
The Cognitive Strategies Behind Multimodal Health Sensemaking: A Menstrual Health Tracking Case Study.

Georgianna Lin, Minh Ngoc Le, Khai Truong, and 1 more author

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2025

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The proliferation of commodity devices for gathering health data has led to multimodal health trackers that provide increasingly holistic views of people’s well-being. As these trackers become more complex, it becomes harder for users to interpret how different signals interrelate in order to derive actionable insights and make informed health decisions. Addressing this challenge first requires understanding the cognitive and behavioral processes through which users interpret and make sense of multimodal data. In this paper, we use menstrual health tracking as a case study for investigating how individuals interpret multimodal health data. We conducted a 100-day longitudinal study with 20 participants who used a variety of health trackers to monitor signals relevant to menstrual health (e.g., hormones, sleep, mood). Through surveys and interviews, we identified that participants aligned their health goals with each device’s perceived scope and approached multimodal data with hypotheses that involved pairs of signals. Our findings shed light on how a person’s confidence in the sensemaking processes shapes their engagement with multimodality, leading to design recommendations that scaffold trust between users and their devices while encouraging exploration and staying true to users’ evolving health goals.

2024

Understanding wrist skin temperature changes to hormone variations across the menstrual cycle

Georgianna Lin, Jin Yi Li, Ken Christofferson, and 3 more authors

npj Women’s Health, Oct 2024

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Consumer devices are increasingly used to monitor peripheral body temperature (PBT) for menstrual cycle tracking, but the link between PBT and hormone variations remains underexplored. This study examines the relationship between these variables with a focus on nightly wrist skin temperature (WST). Fifty participants provided physiological and self-reported data, including WST, daily step counts, glucose levels, hormone levels (E3G, LH), and diary entries. Results show a negative correlation between WST and hormone levels when E3G and LH are below average, and this trend was robust to demographics and self-reported stress. Increased variance between mid-cycle hormonal peaks and WST fluctuations may stem from differences between basal body temperature (BBT) and WST. This research suggests that algorithms reliant on body temperature for tracking hormonal changes or other aspects of the menstrual cycle may need to account for increased variance in WST trends if they are meant to be deployed on wearable devices.
Users’ Perspectives on Multimodal Menstrual Tracking Using Consumer Health Devices

Georgianna Lin, Brenna Li, Jin Yi Li, and 3 more authors

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Oct 2024

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Previous menstrual health literature highlights a variety of signals not included in existing menstrual trackers because they are either difficult to gather or are not typically associated with menstrual health. Since it has become increasingly convenient to collect biomarkers through wearables and other consumer-grade devices, our work examines how people incorporate unconventional signals (e.g., blood glucose levels, heart rate) into their understanding of menstrual health. In this paper, we describe a three-month-long study on fifty participants’ experiences as they tracked their health using physiological sensors and daily diaries. We analyzed their experiences with both conventional and unconventional menstrual health signals through surveys and interviews conducted throughout the study. We delve into the various aspects of menstrual health that participants sought to affirm using unconventional signals, explore how these signals influenced their daily behaviors,and examine how multimodal menstrual tracking expanded their scope of menstrual health. Finally, we provide design recommendations for future multimodal menstrual trackers.
Functional Design Requirements to Facilitate Menstrual Health Data Exploration

Georgianna Lin, Pierre-William Lessard, Minh Ngoc Le, and 4 more authors

In Proceedings of the CHI Conference on Human Factors in Computing Systems, Oct 2024

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Menstrual trackers currently lack the affordances required to help individuals achieve their goals beyond menstrual event predictions and symptom logging. Taking an initial step towards this aspiration, we propose, validate, and refine five functional design requirements for future interface designs that facilitate menstrual data exploration. We interviewed 30 individuals who menstruate and collected their feedback on the practical application of these requirements. To elicit ideas and impressions, we designed two proof-of-concept interfaces to use as design probes with similar core functionalities but different presentations of phase timing predictions and signal arrangement. Our analysis revealed participants’ feedback regarding the presentation of predictions for menstrual-related events, the visualization of future signal patterns, personalization abilities for viewing signals relevant to their menstrual experience, the availability of resources to understand the underlying biological connections between signals, and the ability to compare multiple cycles side-by-side with context.

2023

Blood glucose variance measured by continuous glucose monitors across the menstrual cycle

Georgianna Lin, Rumsha Siddiqui, Zixiong Lin, and 4 more authors

npj Digital Medicine, Oct 2023

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Past studies on how blood glucose levels vary across the menstrual cycle have largely shown inconsistent results based on limited blood draws. In this study, 49 individuals wore a Dexcom G6 continuous glucose monitor and a Fitbit Sense smartwatch while measuring their menstrual hormones and self-reporting characteristics of their menstrual cycles daily. The average duration of participation was 79.3±21.2 days, leading to a total of 149 cycles and 554 phases in our dataset. We use periodic restricted cubic splines to evaluate the relationship between blood glucose and the menstrual cycle, after which we assess phase-based changes in daily median glucose level and associated physiological parameters using mixed-effects models. Results indicate that daily median glucose levels increase and decrease in a biphasic pattern, with maximum levels occurring during the luteal phase and minimum levels occurring during the late-follicular phase. These trends are robust to adjustments for participant characteristics (e.g., age, BMI, weight) and self-reported menstrual experiences (e.g., food cravings, bloating, fatigue). We identify negative associations between each of daily estrogen level, step count, and low degrees of fatigue with higher median glucose levels. Conversely, we find positive associations between higher food cravings and higher median glucose levels. This study suggests that blood glucose could be an important parameter for understanding menstrual health, prompting further investigation into how the menstrual cycle influences glucose fluctuation.
Identifying Multimodal Context Awareness Requirements for Supporting User Interaction with Procedural Videos

Georgianna Lin, Jin Yi Li, Afsaneh Fazly, and 2 more authors

In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, Oct 2023

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Following along how-to videos requires alternating focus between understanding procedural video instructions and performing them. Examining how to support these continuous context switches for the user has been largely unexplored. In this paper, we describe a user study with thirty participants who performed an hour-long cooking task while interacting with a wizard-of-oz hands-free interactive system that is aware of both their cooking progress and environment contexts. Through analysis of the session scripts, we identify a dichotomy between participant query differences and workflow alignment similarities, under-studied interactions that require AI functionality beyond video navigation alone, and queries that call for multimodal sensing of a user’s environment. By understanding the assistant experience through the participants’ interactions, we identify design implications for a smart assistant that can discern a user’s task completion flow and personal characteristics, accommodate requests within and external to the task domain, andsupport nonvoice-based queries.
Understanding tensions in music accessibility through song signing for and with d/Deaf and Non-d/Deaf persons

Suhyeon Yoo, Georgianna Lin, Hyeon Jeong Byeon, and 2 more authors

In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, Oct 2023

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2022

Investigating Culturally Responsive Design for Menstrual Tracking and Sharing Practices Among Individuals with Minimal Sexual Education

Georgianna E Lin, Elizabeth D Mynatt, and Neha Kumar

In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, Oct 2022

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Human-Computer Interaction (HCI) research on menstrual tracking has emphasized the need for more inclusive design of mechanisms for tracking and sharing information on menstruation. We investigate menstrual tracking and data-sharing attitudes and practices in educated, young (20-30 years old) menstruating individuals based in the United States, with self-identified minimal menstrual education backgrounds. Using interviews (N=18), a survey (N=62), and participatory design (N=7), we find that existing mechanisms for tracking and sharing data on menstruation are not adequately responsive to the needs of those who seek relevant menstrual education, are not in the sexual majority, and/or wish to customize what menstrual data they share and with whom. Our analysis highlights a design gap for participants with minimal sexual education backgrounds who wish to better understand their cycles. We also contribute a deepened understanding of structural health inequities that impact menstrual tracking and sharing practices, making recommendations for technology-mediated menstrual care.

2021

Towards finding the optimum position in the visual field for a head worn display used for task guidance with non-registered graphics

Georgianna Lin, Malcolm Haynes, Sarthak Srinivas, and 2 more authors

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Oct 2021

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Where should a HWD be placed in a user’s visual field? We present two studies that compare comfort, preference, task efficiency and accuracy for various HWD positions. The first study offsets a 9.2° horizontal field-of-view (FOV) display temporally (toward the ear) from 0° to 30° in 10° steps. 30° proves too uncomfortable while 10° is the most preferred position for a simple button-pushing game, corroborating results from previous single-task reading experiments. The second experiment uses a Magic Leap One to compare 10° x 10° FOV interfaces centered at line-of-sight, temporally offset 15° (center-right), inferiorly offset 15° (bottom-center), and offset in both directions (bottom-right) for an order picking task. The bottom-right position proved worst in terms of accuracy and several subjective metrics when compared to the line-of-sight position.
Comparing order picking guidance with Microsoft hololens, magic leap, google glass xe and paper

Georgianna Lin, Tanmoy Panigrahi, Jon Womack, and 3 more authors

In Proceedings of the 22nd international workshop on mobile computing systems and applications, Oct 2021

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Head-worn displays (HWDs) are an efficient and cost-effective means to guide users in order picking, a task that requires users to alternate their attention between the physical environment and the HWD’s virtual image. After training 12 participants to expertise in picking, we compare three significantly different HWDs: Magic Leap One, Microsoft Hololens, and Google Glass Explorer Edition against paper pick lists (the industry standard). We find that previous findings on HWD benefits during such tasks are not reflected in all HWDs, suggesting that hardware design significantly influences efficacy. Based on experimental results and observations, we highlight challenges such as head weight, mounting, display clarity, field of view (FOV), and display position and discuss their possible effects on user comfort, user preference, task speed, and task accuracy.
Conversational Partner’s Perception of Subtle Display Use for Monitoring Notifications

Jacob Logas, Georgianna Lin, Kelsie Belan, and 2 more authors

In Proceedings of the Augmented Humans International Conference 2021, Oct 2021

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2020

Conversational greeting detection using captioning on head worn displays versus smartphones

Jason Tu, Georgianna Lin, and Thad Starner

In Proceedings of the 2020 ACM International Symposium on Wearable Computers, Oct 2020

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People who are deaf and hard of hearing often have difficulty realizing when someone is attempting to get their attention, especially when mobile. Speech recognition coupled with a head-worn display (HWD) may aid in awareness of when someone calls the user’s name. As our intended users are often oversubscribed with experiments, we chose to test non-deaf and hard of hearing subjects while refining our procedures. Preliminary findings from three hearing participants wearing sound masking headphones and performing a mobile task suggest that a HWD display may be faster than, and preferred to, a smartphone for displaying captions for attending to one’s name being called.
Towards an Understanding of Real-time Captioning on Head-worn Displays

Jason Tu, Georgianna E Lin, and Thad Starner

In 22nd International Conference on Human-Computer Interaction with Mobile Devices and Services, Oct 2020

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