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

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

• a server configured to:
• receive the one or more sets of first data indicative of one or more impairment patterns collected via one or more impairment monitoring devices in a first set of smart rings, the one or more sets of first data comprising motion data collected via one or more motion sensors of the first smart ring;
• receive the one or more sets of second data indicative of one or more driving patterns collected via one or more driving monitor devices, the one or more driving monitor devices including a second set of smart rings, at least a part of the one or more sets of second data being collected via one or more sensors disposed within the second set of smart rings;
• utilize the one or more sets of first data at least partially collected by the first set of smart rings and the one or more sets of second data at least partially collected by the second set of smart rings as training data for a machine learning (ML) model to train the ML model to discover one or more relationships between the one or more impairment patterns and the one or more driving patterns, wherein the one or more relationships include a relationship representing a correlation between a given impairment pattern and a high-risk driving pattern;
• receive a particular set of data collected via a particular smart ring associated with a user;
• wherein the server is further configured to:
• analyze, via the ML model, the particular set of data collected by the smart ring, wherein the server is configured to:
• determine that the particular set of data represents a particular impairment pattern corresponding to the given impairment pattern correlated with the high-risk driving pattern;
• and
• in response to the determining that the particular set of data represents a particular impairment pattern, predict, via the ML model, a level of risk exposure for the user during driving;
• and
• generate a notification to alert the user of the predicted level of risk exposure.

A server for implementing a machine learning model to predict driving risk exposure based at least in part upon acquired impairment 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 the one or more sets of first data indicative of one or more impairment patterns collected via one or more impairment monitoring devices in a first set of smart rings, the one or more sets of first data comprising motion data collected via one or more motion sensors of the first smart ring;
• receive the one or more sets of second data indicative of one or more driving patterns collected via one or more driving monitor devices, the one or more driving monitor devices including a second set of smart rings, at least a part of the one or more sets of second data being collected via one or more sensors disposed within the second set of smart rings;
• utilize the one or more sets of first data at least partially collected by the first set of smart rings and the one or more sets of second data at least partially collected by the second set of smart rings as training data for a machine learning (ML) model to train the ML model to discover one or more relationships between the one or more impairment patterns and the one or more driving patterns, wherein the one or more relationships include a relationship representing a correlation between a given impairment pattern and a high-risk driving pattern;
• analyze, via the ML model, a particular set of data collected by a particular smart ring associated with a user by:
• determining that the particular set of data represents a particular impairment pattern corresponding to the given impairment pattern correlated with the high-risk driving pattern;
• and
• responding to said determining by predicting, based at least in part upon the ML model, a level of risk exposure for the user during driving;
• and
• generate a notification to alert the user of the predicted level of risk exposure.