SMART RING SYSTEM FOR MONITORING UVB EXPOSURE LEVELS AND USING MACHINE LEARNING TECHNIQUE TO PREDUCT HIGH RISK DRIVING BEHAVIOR
A method for predicting risk exposure can include receiving data from a sensor. The method for predicting risk exposure also can include analyzing the data via a machine learning (ML) model. The analyzing can include determining that the data represents a light exposure pattern correlated with a risk pattern. The ML model can be trained with training data indicative of the light exposure pattern and indicative of the risk pattern to identify a correlation between the light exposure pattern and the risk pattern. The method for predicting risk exposure further can include predicting a risk exposure for a user based on the analyzing the data. The method for predicting risk exposure further can include providing a notice indicating the risk exposure, as predicted. Other embodiments are disclosed herein.
1 . A method for predicting risk exposure, comprising:
receiving data from a sensor;
analyzing the data via a machine learning (ML) model, wherein the analyzing comprises:
determining that the data represents a light exposure pattern correlated with a risk pattern, wherein:
the ML model is trained with training data indicative of the light exposure pattern and indicative of the risk pattern to identify a correlation between the light exposure pattern and the risk pattern;
predicting a risk exposure for a user based on the analyzing the data; and
providing a notice indicating the risk exposure, as predicted.
2 . The method of claim 1 , wherein providing the notice indicating the risk exposure comprises providing the notice indicating the risk exposure to one or more of a ring worn by the user or a mobile phone of the user.
3 . The method of claim 1 , wherein a ring comprises the sensor and the ring is worn by the user.
4 . The method of claim 1 , wherein the notice indicating the risk exposure comprises a score.
5 . The method of claim 1 , wherein the risk exposure comprises one or more of a binary parameter or a ternary parameter.
6 . The method of claim 1 , wherein a vehicle comprises the sensor.
7 . The method of claim 1 , wherein the sensor comprises an electronic driving tracker.
8 . The method of claim 1 , wherein providing the notice indicating the risk exposure comprises providing the notice indicating the risk exposure to a display of a vehicle driven by the user.
9 . The method of claim 1 , further comprising:
comparing the risk exposure to a known threshold to determine whether the risk exposure exceeds the known threshold; and
in response to the risk exposure exceeding the known threshold, generating a system action to warn the user.
10 . A system for predicting risk exposure, comprising:
a server configured to:
receive data from a sensor;
analyze the data via a machine learning (ML) model, comprising:
determining that the data represents a light exposure pattern correlated with a risk pattern, wherein:
the ML model is trained with training data indicative of the light exposure pattern and indicative of the risk pattern to identify a correlation between the light exposure pattern and the risk pattern;
predict a risk exposure for a user based on the analyzing the data; and
provide a notice indicating the risk exposure, as predicted.
11 . The system of claim 10 , wherein the data comprises radiation data.
12 . The system of claim 10 , wherein the data comprises data acquired via a GPS receiver.
13 . The system of claim 10 , wherein the sensor is located in a ring worn by the user.
14 . The system of claim 10 , wherein the server is configured to provide the notice indicating the risk exposure by providing a notification to a mobile phone of the user.
15 . The system of claim 10 , wherein the data comprises light exposure patterns for users other than the user.
16 . The system of claim 10 , wherein the data comprises data for users other than the user.
17 . A non-transitory computer-readable medium storing instructions for implementing a machine learning model to predict risk exposure, wherein the instructions, when executed by one or more processors, cause the one or more processors to:
receive data from a sensor;
analyze the data via a machine learning (ML) model, comprising:
determine that the data represents a light exposure pattern correlated with a risk pattern, wherein:
the ML model is trained with training data indicative of the light exposure pattern and indicative of the risk pattern to identify a correlation between the light exposure pattern and the risk pattern;
predict a risk exposure for a user based on the analyzing the data; and
provide a notice indicating the risk exposure, as predicted.
18 . The non-transitory computer-readable medium of claim 17 , wherein to provide the notice indicating the risk exposure comprises to transmit the notice to one or more of a ring, a vehicle computer, or a mobile phone.
19 . The non-transitory computer-readable medium of claim 17 , wherein the notice indicating the risk exposure compares an estimated amount of vitamin D of the user to a recommended daily amount of vitamin D.
20 . The non-transitory computer-readable medium of claim 17 , wherein the instructions, when executed by one or more processors, further cause the one or more processors to:
compare the risk exposure to a threshold; and
if the risk exposure exceeds the threshold,
generate a system action; and
transmit the system action to a vehicle computer.