Abstract: 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 (SpO2) 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.

Abstract: A system, computer-readable storage medium, and a method capable of, directly or indirectly, estimating sleep states of a user based on sensor data from movement sensors and/or optical sensors.

Abstract: Arousal events can be determined for a user associated with a wearable device, such as a user wearing a wearable computing device including one or more sensors. The one or more sensors may obtain EDA information that may determine a sympathetic nervous system response of the user, which may be responsive to an arousal event or an activation. Detection of events that increase the EDA response may provide information to the user regarding arousal events and provide recommendations to the user to address the arousal events to decrease their response.

Abstract: The present disclosure provides computer-implemented methods, systems, and devices for improved skin temperature monitoring. Accurate estimates of skin and ambient temperature are generated based on determinations and comparisons of skin and internal device temperature sensor measurements contained on or within example devices. The estimates of skin and ambient temperature measurements facilitate monitoring skin and core temperature changes, detecting physiological events of a wearer of example devices, and determining when skin temperature changes are environmentally or physiologically induced.

Abstract: Arousal events can be determined for a user associated with a wearable device, such as a user wearing a wearable computing device including one or more sensors. The one or more sensors may obtain EDA information that may determine a sympathetic nervous system response of the user, which may be responsive to an arousal event or an activation. Detection of events that increase the EDA response may provide information to the user regarding arousal events and provide recommendations to the user to address the arousal events to decrease their response.

Abstract: The present disclosure provides computer-implemented methods, systems, and devices for improved skin temperature monitoring. Accurate estimates of skin and ambient temperature are generated based on determinations and comparisons of skin and internal device temperature sensor measurements contained on or within example devices. The estimates of skin and ambient temperature measurements facilitate monitoring skin and core temperature changes, detecting physiological events of a wearer of example devices, and determining when skin temperature changes are environmentally or physiologically induced.

Abstract: Temperature data acquired from a wearable device, for example at a user's wrist or within the device itself, can be used as a proxy to evaluate core body temperature changes. Sensor data may be provided to determine a skin temperature of a user and also an internal device temperature. A correlation between these two temperatures may be used to monitor subsequent temperature changes, which may be indicative of changes in the user's core body temperature. Temperature changes to the proxy temperature may be evaluated against a threshold to determine whether the user's core body temperature has also increased, which may be indicative of one or more physiological symptoms or events. Furthermore, additional physiological variables such as respiration rate, nocturnal heart rate, and heart rate variability may be analyzed for early signs of impending illness. A trained machine learning classifier can output the predicted illness status of an individual based on these parameters.

Abstract: 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.

Abstract: The present disclosure provides computer-implemented methods, systems, and devices for improved skin temperature monitoring. Accurate estimates of skin and ambient temperature are generated based on determinations and comparisons of skin and internal device temperature sensor measurements contained on or within example devices. The estimates of skin and ambient temperature measurements facilitate monitoring skin and core temperature changes, detecting physiological events of a wearer of example devices, and determining when skin temperature changes are environmentally or physiologically induced.

Abstract: The present disclosure provides computer-implemented methods, systems, and devices for improved skin temperature monitoring. Accurate estimates of skin and ambient temperature are generated based on determinations and comparisons of skin and internal device temperature sensor measurements contained on or within example devices. The estimates of skin and ambient temperature measurements facilitate monitoring skin and core temperature changes, detecting physiological events of a wearer of example devices, and determining when skin temperature changes are environmentally or physiologically induced.

Abstract: A system, computer-readable storage medium, and a method capable of, directly or indirectly, estimating sleep states of a user based on sensor data from movement sensors and/or optical sensors.

Abstract: Arousal events can be determined for a user associated with a wearable device, such as a user wearing a wearable computing device including one or more sensors. The one or more sensors may obtain EDA information that may determine a sympathetic nervous system response of the user, which may be responsive to an arousal event or an activation. Detection of events that increase the EDA response may provide information to the user regarding arousal events and provide recommendations to the user to address the arousal events to decrease their response.

Abstract: Arousal events can be determined for a user associated with a wearable device, such as a user wearing a wearable computing device including one or more sensors. The one or more sensors may obtain EDA information that may determine a sympathetic nervous system response of the user, which may be responsive to an arousal event or an activation. Detection of events that increase the EDA response may provide information to the user regarding arousal events and provide recommendations to the user to address the arousal events to decrease their response.

Abstract: A system, computer-readable storage medium, and a method capable of, directly or indirectly, estimating sleep states of a user based on sensor data from movement sensors and/or optical sensors.

Abstract: 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 (SpO2) 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.

Abstract: 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 (SpO2) 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.

Abstract: 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 (SpO2) 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.

Abstract: A system, computer-readable storage medium, and a method capable of, directly or indirectly, estimating sleep states of a user based on sensor data from movement sensors and/or optical sensors.