Abstract: Methods, systems, and devices for illness detection are described. A method may include receiving heart rate variability (HRV) data associated with a user from a wearable device, the HRV data collected via the wearable device throughout a first time interval and a second time interval subsequent to the first time interval. The method may include inputting the HRV data into a classifier, and identifying a satisfaction of deviation criteria between a first subset of the HRV data collected throughout the first time interval and a second subset of the HRV data collected throughout the second time interval. The method may include causing a graphical user interface (GUI) of a user device to display an illness risk metric for 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.

Abstract: Methods, systems, and devices for detection of medical conditions using respiration rate data are described. A method may include receiving physiological data associated with a user, the physiological data being continuously collected via a wearable device associated with the user, determining a set of respiration rate values for the user over a time interval based on the physiological data, and determining one or more respiration rate parameters associated with a change of the set of respiration rate values over the time interval. The method may further include determining one or more condition risk metrics associated with one or more medical conditions based on the one or more respiration rate parameters, where the one or more condition risk metrics associated with a relative probability that the user is associated with a respective medical condition.

Abstract: Methods, systems, and devices for leveraging data from multiple data sources are described. A method includes receiving physiological data associated with a user from a wearable device and receiving additional data from an external device different from the wearable device, the additional data including at least data associated with characteristics of an environment associated with the user. The method further includes identifying one or more relationships between the collected physiological data and the characteristics of the environment associated with the user, and causing a graphical user interface (GUI) of a user device to display an indication of the one or more relationships, a message associated with the one or more relationships, or both. In some cases, the method includes causing the GUI to display instructions for selectively adjusting the one or more characteristics of the environment associated with the user based on the one or more relationships.

Abstract: Methods, systems, and devices for leveraging data from multiple data sources are described. A method includes receiving physiological data associated with a user from a wearable device and receiving additional data from an external device different from the wearable device, the additional data including at least data associated with characteristics of an environment associated with the user. The method further includes identifying one or more relationships between the collected physiological data and the characteristics of the environment associated with the user, and causing a graphical user interface (GUI) of a user device to display an indication of the one or more relationships, a message associated with the one or more relationships, or both. In some cases, the method includes causing the GUI to display instructions for selectively adjusting the one or more characteristics of the environment associated with the user based on the one or more relationships.

Abstract: Methods, systems, and devices for leveraging data from multiple data sources are described. A method includes receiving physiological data associated with a user from a wearable device and receiving additional data from an external device different from the wearable device, the additional data including at least data associated with characteristics of an environment associated with the user. The method further includes identifying one or more relationships between the collected physiological data and the characteristics of the environment associated with the user, and causing a graphical user interface (GUI) of a user device to display an indication of the one or more relationships, a message associated with the one or more relationships, or both. In some cases, the method includes causing the GUI to display instructions for selectively adjusting the one or more characteristics of the environment associated with the user based on the one or more relationships.

Abstract: Methods, systems, and devices for controlling external devices are described. A method may include receiving physiological data associated with a user from a wearable device, and identifying one or more physiological states, physical activities, or both, associated with the user based on the physiological data. Physiological states may include physiological states associated with waking up, falling asleep, anxiety, relaxation, and the like. The method may further include transmitting an instruction to one or more external devices based on the one or more physiological states, physical activities, or both, where the instruction is configured to selectively modify one or more operational parameters associated with the one or more external devices.

Abstract: Methods, systems, and devices for leveraging data from multiple data sources are described. A method includes receiving physiological data associated with a user from a wearable device and receiving additional data from an external device different from the wearable device, the additional data including at least data associated with characteristics of an environment associated with the user. The method further includes identifying one or more relationships between the collected physiological data and the characteristics of the environment associated with the user, and causing a graphical user interface (GUI) of a user device to display an indication of the one or more relationships, a message associated with the one or more relationships, or both. In some cases, the method includes causing the GUI to display instructions for selectively adjusting the one or more characteristics of the environment associated with the user based on the one or more relationships.

Abstract: Methods, systems, and devices for multiple wearable devices are described. A method may include receiving first physiological data associated with a user from a first wearable device worn at a first position on the user and second physiological data associated with the user from a second wearable device worn at a second position on the user. This method may include determining one or more physiological characteristics associated with the user based on a comparison of the first physiological data and the second physiological data and displaying an indication of the one or more physiological characteristics on a graphical user interface (GUI) of a user device.

Abstract: 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.

Abstract: Methods, systems, and devices for detection of medical conditions using respiration rate data are described. A method may include receiving physiological data associated with a user, the physiological data being continuously collected via a wearable device associated with the user, determining a set of respiration rate values for the user over a time interval based on the physiological data, and determining one or more respiration rate parameters associated with a change of the set of respiration rate values over the time interval. The method may further include determining one or more condition risk metrics associated with one or more medical conditions based on the one or more respiration rate parameters, where the one or more condition risk metrics associated with a relative probability that the user is associated with a respective medical condition.

Abstract: Methods, systems, and devices for illness detection are described. A method may include identifying baseline temperature data associated with a user based on temperature data collected from the user via a wearable device throughout a first time interval. The method may include receiving additional temperature data collected via the wearable device throughout a second time interval, and inputting the baseline temperature data and the additional temperature data into a classifier. The method may include identifying a satisfaction of deviation criteria between the baseline temperature data and the additional temperature data, and causing a graphical user interface (GUI) of a user device to display an illness risk metric for 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.

Abstract: Methods, systems, and devices for illness detection are described. A method may include receiving heart rate variability (HRV) data associated with a user from a wearable device, the HRV data collected via the wearable device throughout a first time interval and a second time interval subsequent to the first time interval. The method may include inputting the HRV data into a classifier, and identifying a satisfaction of deviation criteria between a first subset of the HRV data collected throughout the first time interval and a second subset of the HRV data collected throughout the second time interval. The method may include causing a graphical user interface (GUI) of a user device to display an illness risk metric for 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.

Abstract: Methods, systems, and devices for illness detection are described. A method may include identifying physical activity data, sleep data, or both, associated with a user based on physiological data for the user collected via a wearable device throughout a first and second time interval. The method may include inputting the physical activity data, the sleep data, or both, into a classifier, and identifying, using the classifier, a satisfaction of deviation criteria between a subsets of the physical activity data, the sleep data, or both, collected throughout the first and second intervals. The method may further include causing a graphical user interface (GUI) of a user device to display an illness rick metric associated with the user based at least in part on the satisfaction of the deviation criteria, the illness risk metric associated with a probability that the user will transition from a healthy state to an unhealthy state.

Abstract: Methods, systems, and devices for illness detection are described. A method may include receiving physiological data associated with users, the physiological data being continuously collected via wearable devices associated with the respective users. The method may include identifying baseline physiological data for each user based on a first subset of the physiological data for each respective user. The method may include inputting a second subset of the physiological data and the baseline physiological data for each user into a classifier, and identifying an illness risk metric associated with each user based on the second subset of the physiological data and the baseline physiological data for each respective user.

Abstract: The present disclosure describes a wearable electronic device, having at least one pre-formed stress point that is readily broken when subjected to an excessive external force, thereby causing the wearable to separate, rather than transferring forces, and potential injury, to a user.