Application US20190110755

obtaining calibrated heart rate data from a number of subjects using one or more chest strap type sensors;

obtaining uncalibrated heart rate data from the number of subjects concurrently with the calibrated heart rate data using one or more physiological monitors of a wrist-worn photoplethysmography type;

obtaining feature data from one or more sensors of the one or more physiological monitors of the wrist-worn photoplethysmography type concurrently with the calibrated heart rate data and the uncalibrated heart rate data, the feature data characterizing a plurality of features of a data acquisition context for a corresponding one of the physiological monitors of the wrist-worn photoplethysmography type;

associating a quality metric for the uncalibrated heart rate data with the feature data based on whether, for each data acquisition context, the uncalibrated heart rate data is within a predetermined threshold of the calibrated heart rate data;

creating a quality estimator engine to evaluate a likelihood of the uncalibrated heart rate data being accurate based on the feature data;

receiving second uncalibrated heart rate data and second feature data from a second physiological monitor of the wrist-worn photoplethysmography type;

determining a probability that the second uncalibrated heart rate data is accurate for a window of measurements by calculating a conditional probability that the second uncalibrated heart rate data is accurate based on the second feature data over a distribution of values for the calibrated heart rate data within the window of measurements based on the quality estimator engine; and

associating the probability with the window as a measure of quality for the uncalibrated heart rate data within the window.

obtaining data for a number of subjects, the data including calibrated physiological data from a first type of physiological monitors assumed to be accurate, uncalibrated physiological data from a second type of physiological monitors of unknown accuracy, and feature data characterizing a timewise data acquisition context for the uncalibrated physiological data;

training a quality estimator engine to determine a quality of the uncalibrated physiological data based on the feature data and a difference between the uncalibrated physiological data and the calibrated physiological data;

receiving physiological data and feature data from a different one of the second type of physiological monitors;

calculating a probability that the physiological data is accurate for a window of measurements by applying the quality estimator engine to a distribution of values for the physiological data and corresponding values for the feature data; and

associating the probability with the window as a measure of quality for the physiological data within the window.

receiving physiological data and feature data from one or more sensors of the wearable device, the physiological data characterizing a physiological measurement for a user of the wearable device and the feature data characterizing a timewise data acquisition context for the physiological data;

storing a quality estimator engine that calculates a probability that a measurement of the physiological data is accurate based on corresponding feature data;

calculating a second probability that the physiological data is accurate for a window of measurements by applying the quality estimator engine to a distribution of values for the physiological data and corresponding values for the feature data; and

associating the second probability with the window as a measure of quality for the physiological data within the window.