Oura Health Oy

Gerald Pho

Kirstin Elizabeth Aschbacher

Michael Chapp

Harpreet Singh Rai

Nina Nicole Thigpen

Neta A Gotlieb

Marco Altini

Methods, systems, and devices for illness detection are described. A method may include receiving heart rate variability (HRV) data associated with a user from a wearable device, the HRV data collected via the wearable device throughout a first time interval and a second time interval subsequent to the first time interval. The method may include inputting the HRV data into a classifier, and identifying a satisfaction of deviation criteria between a first subset of the HRV data collected throughout the first time interval and a second subset of the HRV data collected throughout the second time interval. The method may include causing a graphical user interface (GUI) of a user device to display an illness risk metric for the user based on the satisfaction of the deviation criteria, the illness risk metric associated with a relative probability that the user will transition from a healthy state to an unhealthy state.

Illness detection based on nervous system metrics

Methods, systems, and devices for detection of medical conditions using respiration rate data are described. A method may include receiving physiological data associated with a user, the physiological data being continuously collected via a wearable device associated with the user, determining a set of respiration rate values for the user over a time interval based on the physiological data, and determining one or more respiration rate parameters associated with a change of the set of respiration rate values over the time interval. The method may further include determining one or more condition risk metrics associated with one or more medical conditions based on the one or more respiration rate parameters, where the one or more condition risk metrics associated with a relative probability that the user is associated with a respective medical condition.

Identifying conditions using respiration rate

Methods, systems, and devices for miscarriage identification are described. A system may be configured to receive physiological data associated with a user that is pregnant and collected over a plurality of days, where the physiological data includes at least temperature data. Additionally, the system may be configured to determine a time series of temperature values. The system may then identify that the temperature values are lower than a pregnancy baseline of temperature values for the user and detect an indication of an early pregnancy loss of the user. The system may generate a message for display on a graphical user interface on a user device that indicates the indication of the early pregnancy loss.

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Somerville, MA, United States of America

Gerald Pho