Smart ring system for monitoring UVB exposure levels and using machine learning technique to predict high risk driving behavior
The described systems and methods determine a driver's fitness to safely operate a moving vehicle based at least in part upon observed UVB exposure patterns, where the driver's UVB exposure levels may serve as a proxy for vitamin D levels in that driver's body. A smart ring, wearable on a user's finger, continuously monitors user's exposure to UVB light. This UVB exposure data, representing UVB exposure patterns, can be utilized, in combination with driving data, to train a machine learning model, which will predict the user's level of risk exposure based at least in part upon observed UVB exposure patterns. The user can be warned of this risk to prevent them from driving or to encourage them to get more sunlight exposure before driving. In some instances, the disclosed smart ring system may interact with the user's vehicle to prevent it from starting while exposed to high risk due to deteriorated psychological or physiological conditions stemming from insufficient UVB exposure.
1. A method for implementing a machine learning model to predict driving risk exposure based at least in part upon observed UVB exposure patterns, the method comprising:
acquiring, via one or more UVB exposure monitoring devices, one or more sets of first data indicative of one or more UVB exposure patterns;
acquiring, via one or more driving monitor devices disposed on or within a vehicle, one or more sets of second data indicative of one or more driving patterns;
transmitting the one or more sets of first data and the one or more sets of second data to a server;
utilizing the one or more sets of first data and the one or more sets of second data 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 UVB exposure patterns and the one or more driving patterns, wherein the one or more relationships include a relationship representing a correlation between a given UVB exposure pattern and a high-risk driving pattern;
acquiring, via a particular smart ring associated with a user, a particular set of data;
analyzing, via the ML model, the particular set of data collected by the particular smart ring associated with the user, wherein the analyzing includes:
determining that the particular set of data represents a particular UVB exposure pattern corresponding to the given UVB exposure 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
generating a notification to alert the user of the predicted level of risk exposure.
2. The method of claim 1 , wherein acquiring the one or more sets of first data comprises acquiring ultraviolet (UV) radiation data via one or more UV sensors of a smart ring.
3. The method of claim 1 , wherein acquiring the one or more sets of first data comprises acquiring date and latitude data via one or more GPS sensors of a smart ring, and acquiring body temperature data via a one or more temperature sensors of a smart ring.
4. The method of claim 1 , wherein acquiring the one or more sets of first data comprises acquiring skin tone data via one or more spectrophotometers of a smart ring.
5. The method of claim 1 , wherein acquiring the one or more sets of first data comprises receiving user input of the user's skin tone, age, weight, and clothing type.
6. The method of claim 1 , wherein the one or more driving monitor devices includes a vehicle computer or a dedicated electronic driving tracker device;
wherein acquiring the one or more sets of second data comprises acquiring the one or more sets of second data via one or more of the vehicle computer or the dedicated electronic driving tracker device.
7. The method of claim 1 , further comprising:
providing the generated notification at one or more of the particular smart ring, or an in-dash display of the vehicle.
8. The method of claim 1 , further comprising:
comparing the predicted level of risk exposure to a known threshold to determine the predicted level of risk exposure exceeds the known threshold;
responding to determining that the predicted level of risk exposure exceeds the known threshold by generating a system action that prevents the user from operating the vehicle, wherein the said preventing includes preventing the user from starting the vehicle or overtaking control of the vehicle while the vehicle is in operation.
9. The method of claim 1 , further comprising:
utilizing the particular set of data representing the particular UVB exposure pattern to further train the ML model.
10. A system for acquiring data indicative of UVB exposure patterns, and utilizing the data to predict driving risk exposure, comprising:
one or more UVB exposure monitoring devices configured to collect one or more sets of first data indicative of one or more UVB exposure patterns;
one or more driving monitor devices disposed within a vehicle and configured to collect one or more sets of second data indicative of one or more driving patterns;
a smart ring associated with a user that is configured to be worn by the user on a finger of the user; and
a server communicatively coupled to the smart ring, the one or more UVB exposure monitoring devices, and the one or more driving monitor device, the server configured to:
receive the one or more sets of first data and the one or more sets of second data;
utilize the one or more sets of first data and the one or more sets of second data 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 UVB exposure patterns and the one or more driving patterns, wherein the one or more relationships include a relationship representing a correlation between a given UVB exposure pattern and a high-risk driving pattern;
wherein the smart ring is further configured to collect a particular set of data and transmit the particular set of data to the server;
wherein the server is further configured to:
analyze, via the ML model, the particular set of data collected by the smart ring,
determine that the particular set of data represents a particular UVB exposure pattern corresponding to the given UVB exposure pattern correlated with the high-risk driving pattern;
respond to said determining by predicting, 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.
11. The system of claim 10 , wherein the smart ring comprises: one or more UV sensors, wherein the one or more sets of first data include UV radiation data from the one or more UV sensors.
12. The system of claim 10 , wherein the smart ring comprises:
one or more GPS sensors, wherein the one or more sets of first data include date and latitude data from the one or more GPS sensors, and
one or more temperature sensors, wherein the one or more sets of first data include body temperature data from the one or more temperature sensors.
13. The system of claim 10 , wherein the smart ring has an inner diameter within a range between 13 mm and 23 mm.
14. The system of claim 10 , wherein the server is configured to generate the notification to alert the user of the predicted level of risk exposure by way of generating the notification via the particular smart ring, a vehicle computer, or a mobile device in communication with the server.
15. The system of claim 10 , wherein the one or more sets of first data includes UVB exposure pattern data for users other than the user associated with the smart ring.
16. The system of claim 10 , wherein the one or more sets of second data includes driving pattern data for users other than the user associated with the smart ring.
17. A server for implementing a machine learning model to predict driving risk exposure based at least in part upon acquired UVB exposure 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:
detect reception, via the communication interface, of one or more sets of first data, indicative of one or more UVB exposure patterns; and one or more sets of second data indicative of one or more driving patterns;
utilize the one or more sets of first data and the one or more sets of second data 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 UVB exposure patterns and the one or more driving patterns, wherein the one or more relationships include a relationship representing a correlation between a given UVB exposure pattern and a high-risk driving pattern;
analyze, via the ML model, a particular set of data collected by a smart ring by:
determining that the particular set of data represents a particular UVB exposure pattern corresponding to the given UVB exposure 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.
18. The server of claim 17 , wherein causing the one or more processors to generate the notification to alert the user of the predicted level of risk exposure comprises causing the one or more processors to transmit the notification to any of the smart ring, a vehicle computer, or a mobile device.
19. The server of claim 17 , wherein the predicted level of risk exposure is a binary or ternary parameter.
20. The server of claim 17 , wherein the one or more processors are further configured to:
compare the predicted level of risk exposure to a threshold; and
when the predicted level of risk exposure exceeds the threshold, generate a system action and transmit the system action to a vehicle computer for a vehicle to cause the vehicle computer to prevent the user from operating the vehicle.