Multivariate Menstrual Tracking | Blue (Georgianna) Lin

I am interested in creating accurate, inclusive, accessible, and educational menstrual trackers. This work ranges from exploring the nuances for menstrual trackers whom have received minimal sexual education to improving prediction algorithms based on a plethora of physiological signals.

Investigating Menstrual Prediction Mechanisms and Designs for Menstrual Tracking (Advised by Dr. Khai Truong and Dr. Alex Mariakakis)

We collect and analyze a plethora of physiological data collected over multiple menstrual cycles and self-reported qualitative data for better prediction markers. We collect data such as blood glucose, body temperature, respiratory rate, heart rate, sleep cycle, hormones etc. using smart watches, continuous glucose monitors, hormone tests, etc.

Investigating Culturally Responsive Design for Menstrual Tracking and Sharing Practices (Advised by Dr. Neha Kumar and Dr. Elizabeth Mynatt)

Women who often lack familial and societal sex education, as well as access to specialty health care, suffer from a variety of issues from feelings of lack of control to debilitating and unrelievable pain, yet knowing when something is wrong with their menstrual health is rarely afforded by current menstrual trackers.

References

2024

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, 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, 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, 2022

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