MC-Tracking: Towards Ubiquitous Menstrual Cycle Tracking Using the Smartphone.

Tracking the menstrual cycle (MC) is essential for women to manage their health and schedule, especially for those with irregular MC. Existing MC tracking methods either rely on length of previous cycles (e.g., calendar noting) or require additional devices to collect more information (e.g., basal temperature), which are not able to realize both accuracy and convenience. Inspired by the medical studies that gait patterns will be affected by MC, we design a smartphone-based application named MC-Tracking, which monitors MC based on the Inertial Measurement Unit (IMU) signals. By identifying the walking activity based on the acceleration and angular velocity signals, we train an attention-based prediction model that can be generalized to new users with meta learning. 40 volunteers participate in an extensive experiment for more than 3 months, in which more than 2.4 TB of time-series data is collected to evaluate the performance of MC-tracking. It is verified that MC-tracing can predict the onset of MC seven days in advance with an average error of 0.56 days. We also demonstrate that the prediction accuracy is robust to the age, emotion, biological clock and smartphone brand.