Provided herein are devices and methods for determining or predicting ovulation occurrence in human subjects, by utilizing non-invasive measurements of various activity parameters, and determination of patterns thereof over a period of time.

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.

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.

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.

An electronic system for determining a fertility phase of a female human comprises a wearable device (1) that includes a first sensor system (101), configured to determine heart rate and heart rate variability of the female human, and a second sensor system (102), configured to determine acceleration of the female human. The electronic system further comprises a processor (13, 30), configured to detect during menstrual cycles sleep phases with resting pulse, using the heart rate variability and the acceleration, to determine a change in the resting pulse during a menstrual cycle using the heart rate, and to determine the fertility phase using a time of the change in the resting pulse. The processor for determining the fertility phase is implemented in the wearable device (1), in a mobile communication device (4), and/or in a cloud-based computer system (3).

Health information for a woman can be used to predict timing of events related to the woman's menstrual cycle. If available, historical cycle information for a woman can be used to predict upcoming cycle events, such as the start and stop of menstruation. To improve the accuracy of those predictions, one or more health metrics are monitored for the woman that can be correlated with the menstrual cycle. These can include, for example, the resting heart rate (RHR), blood oxygen concentration (SpO.sub.2) level, and hemoglobin concentration, among other such options. The metrics are monitored over time to determine patterns that can be correlated with menstrual cycle. This information can then be used to update the predictive model, as well as to update individual event predictions. Information about the predictions, and updates to the predictions, can be surfaced accordingly.

An electronic system for detecting events related to a pregnancy of a female human, such as ovulation, conception, and miscarriage, comprises a wearable device (1) with a sensor system (100) worn in contact with the skin for measuring one or more physiological parameters. A processor (13, 30, 40) is configured to receive a user entry indicating a time of actual menses, and to determine time windows, for analyzing physiological parameters of the female human, using the time of actual menses. The processor is further configured to detect the pregnancy related events by comparing the physiological parameters, determined and recorded for a first time window, with those determined and recorded for a second time window, to indicate the pregnancy related events when defined detection criteria are met, and to use the user input for pregnancy related events to optimize the detection of these events with machine learning trained algorithms.

The present invention solves a conventional problem that it is not possible to predict menstruation-related information related to menstruation using abdominal sounds. A menstruation-related information output apparatus includes: a learning information storage unit in which learning information configured using two or more pieces of training data having sound information acquired from abdominal sounds of a user and menstruation-related information related to menstruation is stored; a sound information acquiring unit that acquires sound information from abdominal sounds of the user; a predicting unit that applies the learning information to the sound information acquired by the sound information acquiring unit, thereby acquiring menstruation-related information; and an output unit that outputs the menstruation-related information acquired by the predicting unit. Accordingly, it is possible to predict menstruation-related information using abdominal sounds from the abdomen.

Health information for a woman can be used to predict timing of events related to the woman's menstrual cycle. If available, historical cycle information for a woman can be used to predict upcoming cycle events, such as the start and stop of menstruation. To improve the accuracy of those predictions, one or more health metrics are monitored for the woman that can be correlated with the menstrual cycle. These can include, for example, the resting heart rate (RHR), blood oxygen concentration (SpO.sub.2) level, and hemoglobin concentration, among other such options. The metrics are monitored over time to determine patterns that can be correlated with menstrual cycle. This information can then be used to update the predictive model, as well as to update individual event predictions. Information about the predictions, and updates to the predictions, can be surfaced accordingly.

Physiological metrics such as respiratory rate, resting heart rate, heart rate variability, temperature, and the like can be measured over time for a user and correlated to reproductive phases. By determining the chronological phase in a hormonal cycle or the like, automated recommendations for sleep, diet, exercise and the like can be provided in a phase-coordinated manner.