Proxy model using mobile device data to provide health indicators

A computer system configured to train an artificial neural network for health assessment, the computer system comprising:

• one or more processors;
• and
• one or more memories storing computer-executable instructions that, when executed by the one or more processors, cause the computing system to:
• collect labeled training data for the artificial neural network, the labeled training data including a plurality of activity data training sets corresponding to a plurality of training persons, wherein each activity data training set of the plurality of activity data training sets includes a respective plurality of personal activity metrics obtained or derived from one or more respective mobile electronic devices associated with a respective training person of the plurality of training persons, and the each activity data training set is labeled with a respective known health indicator assigned to the respective training person by a health assessment process;
• train the artificial neural network with the labeled training data by at least identifying, from the respective plurality of personal activity metrics in the plurality of activity data training sets, one or more significant metrics having a significant predictive effect upon the respective known health indicator, and one or more insignificant metrics not having a significant predictive effect upon the respective known health indicator, wherein the artificial neural network is trained when the artificial neural network determines a respective output for the each activity data training set that matches the respective known health indicator associated with the each activity data training set;
• obtain an activity data set corresponding to a target person, the activity data set including a plurality of personal activity metrics obtained or derived from one or more mobile electronic devices associated with the target person;
• receive, by the artificial neural network, as trained, the activity data set;
• process, by the artificial neural network, as trained, the activity data set corresponding to the target person;
• determine, by the artificial neural network, as trained, a health indicator of the target person based upon processing the activity data set corresponding to the target person;
• and
• output, by the artificial neural network, as trained, the health indicator of the target person, as determined;
• wherein to obtain the activity data set includes:
• to activate one or more sensors to obtain sensor data associated with the one or more significant metrics, and
• to deactivate the one or more sensors to not obtain sensor data associated with the one or more insignificant metrics.

A non-transitory, computer-readable medium for training an artificial neural network for health assessment, the non-transitory, computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to:

• collect labeled training data for the artificial neural network, the labeled training data including a plurality of activity data training sets corresponding to a plurality of training persons, wherein each activity data training set of the plurality of activity data training sets includes a respective plurality of personal activity metrics obtained or derived from one or more respective mobile electronic devices associated with a respective training person of the plurality of training persons, and the each activity data training set is labeled with a respective known health indicator assigned to the respective training person by a health assessment process;
• train the artificial neural network with the labeled training data by at least identifying, from the respective plurality of personal activity metrics in the plurality of activity data training sets, one or more significant metrics having a significant predictive effect upon the respective known health indicator, and one or more insignificant metrics not having a significant predictive effect upon the respective known health indicator, wherein the artificial neural network is trained when the artificial neural network determines a respective output for the each activity data training set that matches the respective known health indicator associated with the each activity data training set;
• obtain an activity data set corresponding to a target person, the activity data set including a plurality of personal activity metrics obtained or derived from one or more mobile electronic devices associated with the target person;
• receive, by the artificial neural network, as trained, the activity data set;
• process, by the artificial neural network, as trained, the activity data set corresponding to the target person;
• determine, by the artificial neural network, as trained, a health indicator of the target person based upon processing the activity data set corresponding to the target person;
• and
• output, by the artificial neural network, as trained, the health indicator of the target person, as determined;
• wherein to obtain the activity data set includes:
• to activate one or more sensors to obtain sensor data associated with the one or more significant metrics, and
• to deactivate the one or more sensors to not obtain sensor data associated with the one or more insignificant metrics.