Abstract: Methods and systems for monitoring of sensor data for processing by machine-learning models to generate event predictions to estimate a risk a medical event are provided. An electronic device or wearable smart device may monitor the output of various sensors to collect data related to a person's activity level, location changes, and communications and may use this information as input to a personalized trained machine-learning model to predict a likelihood of an event.

Abstract: The present disclosure relates to systems and methods for detecting sleep-wake activity of a subject using change-point events determined from physiological and/or movement measures. In one implementation, the method may include obtaining at least one set of sensor data generated by one or more sensors for a period of time. The method may also include generating at least two measures from the at least one set of sensor data. The method may further include determining a series of change point events for each measure for the period of time. The method may include determining a sleep stage for each interval of the period of time from at least two sleep stages by processing the series of change point events for each measure using a sleep stage classifier. The sleep stage classifier may include a set of parameters for each measure.

Abstract: A computer-implemented method includes obtaining movement data associated with a subject and measured by a stationary motion sensor. The movement data includes a series of values representing a series of movement events of the subject crossing fields of view of the stationary motion sensor. Each movement event in the series of movement events is associated with a respective time stamp. The computer-implemented method further includes extracting a plurality of features from the movement data, determining that the movement data is consistent with symptoms of an illness using a machine-learning model and based upon the plurality of features, and generating an output indicating a result of the determination.

Abstract: Methods and systems for monitoring of sensor data for processing by machine-learning models to generate event predictions to estimate a risk a medical event are provided. An electronic device or wearable smart device may monitor the output of various sensors to collect data related to a person's activity level, location changes, and communications and may use this information as input to a personalized trained machine-learning model to predict a likelihood of an event.