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.

An electronic system for non-invasive monitoring of estrogen of a female human comprises a wearable device (1) and a processor (13, 30, 40). The wearable device (1) includes a first sensor system (101) configured to be worn in contact with the skin of the female human and to determine a level of perfusion of the female human. The processor (13, 30, 40) is configured to receive and store the level of perfusion of the female human from the first sensor system (101) for a respective point in time. The processor (13, 30, 40) is further configured to determine a change in the level of perfusion of the female human, using the levels of perfusion of the female human stored for a plurality of respective points in time. Furthermore, the processor (13, 30, 40) is configured to detect a change in estrogen level of the female human based on the change in the level of perfusion of the female human.

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.

A fertility tracking system is disclosed. The system includes a sensual fertility device that provides stimulation to the woman while also providing feedback regarding the status of the user's menstrual cycle in the form of changing colors of the sensual fertility device. The system further includes systemic software (such as a mobile application) that communicates with the sensual fertility device to determine and instruct which color should be activated on the sensual fertility device.

The invention relates to a method for predicting and testing physiological conditions of a female mammal related to an increased level of ferning present in a dried mucous body fluid sample of the female mammal, comprising: capturing an image of the dried mucous body fluid sample via a camera of a mobile telecommunication device through a magnifying lens releasably coupled to an objective of the camera, detecting the presence of crystals in the sample by processing the image, determining the crystal density within the sample from the detected crystals, predicting the physiological condition of the female mammal by comparing the crystal density to reference crystal density data, wherein increased crystal density is indicative of increased ferning level. The invention further relates to a mobile telecommunication device comprising: at least one processing unit, a user input interface, a display, a camera having an objective, a magnifying lens releasably coupled to the objective, at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processing unit, cause the device to perform at least the following: capture an image of a dried mucous body fluid sample of a female mammal via the camera through the magnifying lens, detect the presence of crystals in the sample by processing the image, determine crystal density within the sample from the detected crystals, predict a physiological condition of the female mammal related to an increased level of an ferning by comparing the crystal density to at least one reference density, wherein increased crystal density is indicative of increased ferning level.

An electronic system for non-invasive monitoring of estrogen of a female human comprises a wearable device and a processor. The wearable device includes a first sensor system configured to be worn in contact with the skin of the female human and to determine a level of perfusion of the female human. The processor is configured to receive and store the level of perfusion of the female human from the first sensor system for a respective point in time. The processor is further configured to determine a change in the level of perfusion of the female human, using the levels of perfusion of the female human stored for a plurality of respective points in time. Furthermore, the processor is configured to detect a change in estrogen level of the female human based on the change in the level of perfusion of the female human.

Medical implants including sensors are described. The implant may have a flexible shell with a base, wherein the plurality of sensors may be incorporated onto and/or into the shell. The plurality of sensors may be distributed at different locations of the shell. Each sensor of the plurality of sensors may be configured to measure one or more parameters such as, e.g., temperature and/or pressure, and may comprise an electromagnetic coil useful for wireless transmission of data. Each sensor may be configured to transmit data at a frequency different from the frequency of one or more of the other sensors, may be configured to transmit data at a time different from a time data is transmitted by one or more of the other sensors, and/or may include a device identifier different from the device identifier of one or more of the other sensors.

According to certain embodiments, an electronic device comprises: a sensor module; a processor operatively connected to the sensor module; and a memory operatively connected to the processor, wherein the memory stores instructions that, when executed, cause the processor to perform a plurality of operations comprising: acquiring sensor information through the sensor module, determining sleep information of a user, based on the sensor information, and determining a biorhythm of the user, based on the sleep information.