Abstract: Systems and methods for food logging are disclosed herein. In some embodiments, a food logging system converts text inputs that describe an instance of dietary intake in the form of unstructured text to a detailed description of the nutritional content of the instance in the form of structured data. The food logging system can consist of five structural components, each of which performs a specific task. Each food logging system component is associated with a defined resource that provides information and directives on how the associated component is to perform its designated task. Together, these components and associated resources constitute an apparatus that enables users to carry out detailed and accurate food logging, simply by providing textual descriptions of their dietary intake.

Abstract: Wearable blood pressure biosensors, systems, and methods for short-term blood pressure predictions are disclosed herein. In some embodiments, a blood pressure prediction system is configured to provide short-term predictions of average blood pressures for future time period(s) or horizon(s) (e.g., for a coming week). The system can include models trained to predict future systolic values, diastolic blood pressure values, and/or trends. The models can be trained on data related to personalized body, health, and/or physical characteristics of the user, e.g., the current or previous blood pressure, amount of sleep, heart rate, blood glucose (BG), activity, weight, etc. In some embodiments, the models can also determine whether the blood pressure of the user will change or remain relatively constant over a period/range of time.

Abstract: Systems and methods for biomonitoring and personalized healthcare are disclosed herein. In some embodiments, a biomonitoring and healthcare guidance system is configured to generate personalized self-care recommendations (e.g., recommendations relating to sleep, exercise, diet, etc.) to guide a. patient in effectively managing and/or improving a chronic condition (e.g., diabetes, pre-diabetes, hypertension, hyperlipidemia, etc.). The system can continuously or periodically update and/or adapt the self-care recommendations (e.g., based on data from the particular patient as well as data from a plurality of other patients). The system can guide individuals toward self-care changes that are likely to improve their chronic health conditions, support them in making those changes, and adapt and/or update continuously over time.

Abstract: Systems and methods for biomonitoring and personalized healthcare are disclosed herein. The method can include obtaining new data and accessing one or more user history items regarding a user; estimating a state of the user; identifying and executing an action for affecting a response of the user in assisting the user adjust a user behavior; and updating a model based on the response of the user.

Abstract: Systems, devices, and methods for biomonitoring are disclosed herein. In some embodiments, a device for monitoring a user's health comprises a patch including a substrate configured to couple to the user's skin, and an array of microneedles carried by the substrate. The array of microneedles can be configured to access interstitial fluid in the user's skin and generate a first electrical signal indicative of at least one analyte in the interstitial fluid. The device can include a pod configured to releasably couple to the patch, the pod having at least one sensor configured to generate a second electrical signal indicative of a physiological parameter of the user. The pod can further include a processor configured to receive and process the first and second electrical signals to generate health measurements for the user. The pod can also include a communication unit configured to transmit the health measurements to a remote device.

Abstract: Systems and methods for forecasting and/or explaining health metrics are provided. In some embodiments, for example, a method for informing a user of at least one health factor contributing to a predicted health metric comprises receiving health data of the user, and calculating a plurality of features based on the health data. The features can be categorized into a plurality of feature groups. The method further includes generating a prediction of a health metric of the user by inputting the features into a machine learning model. The method can also include identifying at least one contributing health factor by selecting at least one feature group that provides a threshold contribution to the prediction, and determining a health factor associated with the at least one feature group. The method can include outputting a notification for the user including the prediction and the at least one contributing health factor.

Abstract: Systems and methods for biomonitoring and personalized healthcare are disclosed herein. In some embodiments, a computer-implemented method for forecasting a blood glucose state of a patient is provided. The method comprises: receiving blood glucose data of the patient; generating at least one initial prediction of the blood glucose state by inputting the blood glucose data into a first set of machine learning models; determining a plurality of features at least partly from the at least one initial prediction; and generating a final prediction of the blood glucose state by inputting the plurality of features into a second set of machine learning models.

Abstract: A method, a system and a computer program product for forecasting blood glucose concentration. One or more features for training a blood glucose concentration forecasting model are determined. The features are determined based on one or more input data parameters associated with a user in a plurality of users. Using the determined one or more features, the blood glucose concentration forecasting model is trained. Using the trained blood glucose concentration forecasting model, one or more expected blood glucose concentrations for the user are generated.