Described herein are various principles related to collecting and analyzing fertility data for female humans. The underlying concept is that a woman's hormones fluctuate throughout the menstrual cycle, affecting optimal exercise routines and general health practices. A dedicated sensor may be used to collect fertility data, or an estimate may be derived from the individual's menstrual history. Once collected or estimated, the fertility data is factored with other variables to determine the optimal exercise routine or general health habits for the woman. The recommendations are communicated to the woman, who may provide feedback to further improve future recommendations.

The present invention relates to a method for calculating the body condition scoreBCS, the weight of an animal and its state of fertility by means of the mathematical processing of some characteristic morphological traits of the observed subject, which makes use of at least one contact or no-contact detection device of the profile 109 of the animal, at least a data processing unit and a program that implements a specific mathematical method of interpretation. By such a method, the determination of the body condition and its synthetic index or fattening index or FI, is independent of species, race, gender, age and absolute size of the examined animal. This method is also robust to possible errors of positioning of the apparatus by an operator.

The present invention discloses software programs, systems and methods for increasing the chances of users conception by maximizing the user's fertility potential by providing a software program that combines identifying the underlying causes of infertility and using best practices as described by current medical literature for fertility changes along with herbal recommendations to provide personalized wellness recommendations that give the individual user the power to improve their natural fertility.

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.

Embodiments are directed to systems and methods for tracking menstrual cycles of a user. Embodiments can include obtaining a first set of temperature data at an electronic device, and in response to the first set of temperature data satisfying a first criteria, determining a first probability that ovulation occurred during a first time period using the first set of temperature data. In response to the first probability meeting a second criteria, embodiments can include determining a second set of probabilities comprising a probability that ovulation occurred for each day of a first set of days within the first time period. An estimated ovulation date can be determined using the second set of probabilities, and an electronic device can display an output indicating the estimated ovulation date.

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.

Methods, systems, and devices for identifying irregular cycles, polycystic ovary syndrome (PCOS), and endometriosis based on wearable-based physiological data are described. A system may be configured to receive physiological data associated with a user collected via a wearable device, the physiological data collected throughout at least a portion of a menstrual cycle for the user. The system may be configured to determine a time series of a plurality of physiological measurements based on the physiological data, and identify that the physiological measurements deviate from a baseline measurements associated with the user, other users, or both. The system may then identify one or more risk metrics associated with relative probabilities that the user is experiencing PCOS, endometriosis, or both, and may generate a message for display on a graphical user interface (GUI) on a user device that indicates information associated with the one or more risk metrics.

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.

A method and apparatus for determining at least one representative temperature value for a female human user for an extended period includes receiving at least a first, second and third plurality of temperature measurements obtained from a female human user during at least first, second and third respective extended periods, wherein each extended period includes at least one hour and wherein the start of each extended period is separated by at least 8 hours. At least one representative temperature value is calculated for the second extended period. The representative temperature value is calculated using at least one first temperature value obtained from a plurality of measurements taken during the first extended period, at least one second temperature value obtained from a plurality of measurements taken during the second extended period and at least one third temperature value obtained from a plurality of measurements taken during the third extended period.

An information processing device determines a short term length and a long term length so that a timing(s) at which a short-term moving average of a plurality of days of body temperatures measured during past menstrual cycles falls below a long-term moving average of the body temperatures coincides with a menstrual date. The information processing device identifies the timing at which a short-term moving average calculated with the determined short term length exceeds a long-term moving average calculated with the determined long term length, of body temperatures measured during a target menstrual cycle. Based on the identified timing, the information processing device predicts the next menstrual date or estimates the arrival of an ovulation date.