Smart ring system for measuring stress levels and using machine learning techniques to predict high risk driving behavior

A system for acquiring data indicative of stress patterns, and utilizing the data to predict driving risk exposure, comprising:

• a server communicatively coupled to a smart ring, the server configured to:
• receive a machine learning (ML) model that is trained to determine a correlation between a stress pattern and a driving pattern using a set of first data and a set of second data, wherein:
• the set of first data is indicative of the stress pattern collected via a stress monitoring device;
• and
• the set of second data is indicative of the driving pattern collected via one or more sensors disposed on or within a vehicle;
• detect a first cortisol level of a user from the smart ring worn by the user prior to a driving session;
• analyze, via the ML model, the first cortisol level of the user prior to the driving session, wherein the server is configured to:
• determine whether the first cortisol level of the user represents a level of correspondence between a particular stress pattern of the user and the stress pattern correlated with the driving pattern;
• predict, based upon the level of correspondence between the particular stress pattern of the user and the stress pattern correlated with the driving pattern, a level of driving risk for the user prior to the driving session;
• and
• generate a first notification prior to the driving session, wherein the first notification provides the user with a remediating action comprising a first stress coping strategy, based on the level of driving risk for the user as predicted prior to the driving session;
• transmit the first notification to the user;
• after transmitting the first notification:
• detect a second cortisol level of the user during the driving session from the smart ring worn by the user;
• perform a correlation of real-time stress data to a driving compliance of the user with the remediating action, wherein the real-time stress data is based on the second cortisol level of the user and a behavior of the user during the driving session;
• based on the correlation, determine a new remediating action;
• generate a second notification, wherein the second notification provides the user with:
• an alert to display to the user (i) the level of driving risk for the user during the driving session, and (ii) the new remediating action;
• and
• transmit the second notification to the user.

A server for predicting driving risk exposure based at least in part upon acquired stress patterns, the server comprising:

• a communication interface;
• one or more processors coupled to the communication interface;
• and
• a memory coupled to the one or more processors and storing computer readable instructions that, when implemented, cause the one or more processors to:
• receive a machine learning (ML) model that is trained to determine a correlation between a stress pattern and a driving pattern using a set of first data and a set of second data, wherein:
• the set of first data is indicative of the stress pattern collected via a stress monitoring devices;
• and
• the set of second data is indicative of the driving pattern collected via one or more sensors disposed on or within a vehicle;
• detect a first cortisol level of a user from a smart ring worn by the user prior to a driving session;
• analyze, via the ML model, the first cortisol level of the user prior to the driving session, wherein the processor is configured to:
• determine whether the first cortisol level of the user represents a level of correspondence between a particular stress pattern of the user and the stress pattern correlated with the driving pattern;
• and
• predict, based upon the level of correspondence between the particular stress pattern of the user and the stress pattern correlated with the driving pattern, a level of driving risk for the user prior to the driving session;
• generate a first notification prior to the driving session, wherein the first notification provides the user with a remediating action comprising a first stress coping strategy, based on the level of driving risk for the user as predicted prior to the driving session;
• transmit the first notification to the user;
• after transmitting the first notification:
• detect a second cortisol level of the user during the driving session from the smart ring worn by the user;
• perform a correlation of real-time stress data to a driving compliance of the user with the remediating action, wherein the real-time stress data is based on the second cortisol level of the user and a behavior of the user during the driving session;
• based on the correlation, determine a new remediating action;
• generate a second notification, wherein the second notification provides the user with:
• an alert to display to the user (i) the level of driving risk for the user during the driving session, and (ii) the new remediating action;
• and
• transmit the second notification to the user.