Provided are systems for grouping users who have chosen to participate into one of a plurality of menstrual cycle groups based on data provided by and/or collected from those users. In some examples, a wearable computing device can include one or more sensors that can measure one or more physiological signals associated with the user. Based on the physiological signals gathered from the one or more sensors, the wearable computing device can determine biometric data for one or more users. Furthermore, the wearable computing device can enable a user to submit information about their menstrual cycle (e.g., via an interactive touch screen). These factors can be used to automatically determine some menstrual cycle data for a user.

The invention relates to a system (4) for estimating a fertility status of a woman, particularly for determining a conception probability of a woman, the system (4) comprising:—A wearable device (2A, 2B), comprising at least one sensor (201, 203, 204) configured to record at least one physiological signal from a woman wearing the wearable device (2A, 2B) and to generate sensor data from the at least one physiological signal, wherein the wearable device (2A, 2B) is configured and arranged to provide the sensor data to—An evaluation system (1) configured and arranged to receive and process the sensor data from the wearable device (2A, 2B), wherein the evaluation system (1) is further configured and arranged to classify the sensor data into at least a first group and a second group, wherein the first group is associated to sensor data indicative of a woman having a high fertility status and wherein the second group is associated to sensor data indicative of a woman having a low fertility status.

Methods, systems, and devices for illness detection are described. A method may include identifying a menstrual cycle model associated with a menstrual cycle for a user, and receiving physiological data for the user collected throughout a first time interval and a second time interval by a wearable device. The method may include inputting the physiological data and the menstrual cycle model into a classifier, and identifying a satisfaction of deviation criteria between first and second subsets of the physiological data collected throughout the first and second time intervals, respectively, based on the menstrual cycle model. The method may include causing a graphical user interface (GUI) of a user device to display an illness risk metric associated with 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.

A mechanism for estimating windows for menstrual cycles and fertility. The mechanism tracks menstrual cycles based on accuracy of predictions as compared to user input logging the start and stop of a cycle; this user input and the accuracy of prior cycle predictions is used to improve and update future predictions. Thus, as the mechanism receives additional data, it refines its predicted fertility window and period. A user's heart rate may be used to estimate fertility windows, periods, and other portions of a menstrual cycle.

A method and system are disclosed for determining for a female with irregular menstrual cycles a time interval of fertility, comprising receiving, in a processor (11), from a sensor system (22) of a wearable device (2) of the female, physiological data; determining an estimated time to ovulation of the female, by use of a machine learning model and the physiological data; determining the time interval of fertility using pre-determined time thresholds; and generating a message for the female indicating the time interval of fertility.

Embodiments of a vaginal temperature sensing apparatus, a visually sense-able battery power-on indicator (16), manufacturing with cure temperatures that protect a battery, substantially error-free, user-initiated device activation componentry (30) to start battery power, and a timer to automatically terminate flow of battery power. Data can, by an automatic data transform recalculator (138) with body temperature dips in transformed and recalculated diurnal high body temperatures, predict an ovulation event and provide an indication through a zenith based ovulation indicator (106). Systems can include neural network based artificial intelligence to automatically self-improve by using historical or even other, multi user data and user input and improve its indication result.

Provided is a technology that allows estrus of a sow to be accurately determined without relying on experience or in intuition of an observer. An estrus determination device for a sow includes a measurement unit that measures, per unit time, a frequency of standing up and lying down of a sow raised in a stall and a determination unit that determines estrus of the sow on the basis of a plurality of frequencies repetitively measured by the measurement unit over a set given period.

Embodiments of a vaginal temperature sensing apparatus, a visually sense-able battery power-on indicator (16), manufacturing with cure temperatures that protect a battery, substantially error-free, user-initiated device activation componentry (30) to start battery power, and a timer to automatically terminate flow of battery power. Data can by an automatic data transform recalculator (138) with body temperature dips in transformed and recalculated diurnal high body temperatures predict an ovulation event and provide an indication through a zenith based ovulation indicator (106). Systems can include neural network based artificial intelligence to automatically self-improve by using historical or even other, multi user data and user input and improve its indication result.

A method, process, or software package configured to receive user inputs related to health, calculate a diagnosis based upon user inputs and historical data, calculate a treatment plan based upon user inputs and historical data, and present the diagnosis and a treatment plan to the user via a graphical user interface.

An electronic system for determining a time of the temperature nadir of a female human during the menstrual cycle comprises a wearable device (1) that includes a first sensor system (104), configured to determine a temperature of the female human, and a second sensor system (101, 102, 103), configured to determine one or more further physiological parameters of the female human. The electronic system further comprises a processor (13, 30, 40), configured to determine a detected starting point of the fertility phase of the female human, using the one or more further physiological parameters of the female human. The processor (13, 30, 40) is further configured to detect the temperature nadir as a temporary decrease in the temperature, using the detected starting point of the fertility phase of the female human. The time of the temperature nadir is used as an indicator of the time of ovulation and peak oestrogen level.