SMART RING SYSTEM FOR MONITORING SLEEP PATTERNS AND USING MACHINE LEARNING TECHNIQUES TO PREDICT HIGH RISK DRIVING BEHAVIOR
A method for implementing a machine learning model to predict risk exposure can include acquiring, via a sleep detecting device associated with a user, a set of user data. The method for implementing the machine learning model can also include predicting, by at least a trained ML model, a level of risk exposure for an other activity for the user. The trained ML model can be trained utilizing data indicative of one or more sleep patterns and data indicative of the other activity to identify one or more relationships between the one or more sleep patterns and the level of risk exposure for the other activity. The method can further include generating a notification to alert the user of the level of risk exposure, as predicted, for the other activity. Other embodiments are disclosed.
1 . A method for implementing a machine learning model to predict risk exposure, the method comprising:
acquiring, via a sleep detecting device associated with a user, a set of user data including data collected by a sensor disposed in the sleep detecting device;
predicting, by at least a trained ML model, a level of risk exposure for an other activity for the user, wherein input for the trained ML model is based on at least the set of user data, wherein the trained ML model is trained utilizing one or more sets of first data indicative of one or more sleep patterns collected by the sleep detecting device and one or more sets of second data indicative of the other activity to identify one or more relationships between the one or more sleep patterns and the level of risk exposure for the other activity; and
generating a notification to alert the user of the level of risk exposure, as predicted, for the other activity.
2 . The method of claim 1 , wherein:
the sleep detecting device is configured to interact with one or more of a wearable device, a mobile device, or a vehicle interface.
3 . The method of claim 1 , further comprising:
transferring energy to the sleep detecting device via a charging device while the user is wearing the sleep detecting device.
4 . The method of claim 1 , wherein:
the one or more sets of first data include one or more of a heart rate variability of the user, a blood pressure of the user, a body temperature of the user, a skin conductance of the user, a skin perfusion of the user, a sweat amount of the user, a sweat concentration of a substance of the user, or a body movement measurement of the user; and
a date and time stamp associated with the one or more sets of first data.
5 . The method of claim 1 , wherein:
the one or more sets of second data include one or more of an acceleration measurement, a braking measurement, a swerving measurement, a proximity measurement to another vehicle, an adherence to a road marker measurement, or a speed measurement.
6 . The method of claim 1 , wherein:
one or more other detecting devices collect the one or more sets of second data indicative of the other activity; and
the one or more other detecting devices include one or more of a speedometer, an accelerometer, a camera, an image sensor, a laser sensor, a RADAR sensor, an infrared sensor, a GPS receiver, or a compass.
7 . The method of claim 1 , further comprising:
transmitting the notification for display on an electronic device, wherein the notification comprises one or more of a score, a figure, a graph, a symbol, or a color field to alert the user of the level of risk exposure, as predicted, for the other activity.
8 . A system for acquiring data indicative of sleep patterns, and utilizing the data to predict risk exposure, comprising:
one or more processors configured to:
acquire, via a sleep detecting device associated with a user, a set of user data including data collected by a sensor disposed in the sleep detecting device;
predict, by at least a trained ML model, a level of risk exposure for an other activity for the user, input for the trained ML model is based on at least the set of user data, wherein the trained ML model is trained utilizing one or more sets of first data indicative of one or more sleep patterns collected by the sleep detecting device and one or more sets of second data indicative of the other activity to identify one or more relationships between the one or more sleep patterns and the level of risk exposure for the other activity; and
generate a notification to alert the user of the level of risk exposure, as predicted, for the other activity.
9 . The system of claim 8 , wherein:
the sleep detecting device is configured to interact with one or more of a wearable device, a mobile device, or a vehicle interface.
10 . The system of claim 8 , further comprising:
transferring energy to the sleep detecting device via a charging device while the user is wearing the sleep detecting device.
11 . The system of claim 8 , wherein:
the one or more sets of first data include one or more of a heart rate variability of the user, a blood pressure of the user, a body temperature of the user, a skin conductance of the user, a skin perfusion of the user, a sweat amount of the user, a sweat concentration of a substance of the user, or a body movement measurement of the user; and
a date and time stamp associated with the one or more sets of first data.
12 . The system of claim 8 , wherein:
the one or more sets of second data include one or more of an acceleration measurement, a braking measurement, a swerving measurement, a proximity measurement to another vehicle, an adherence to a road marker measurement, or a speed measurement.
13 . The system of claim 8 , wherein:
one or more other detecting devices collect the one or more sets of second data indicative of the other activity; and
the one or more other detecting devices include one or more of a speedometer, an accelerometer, a camera, an image sensor, a laser sensor, a RADAR sensor, an infrared sensor, a GPS receiver, or a compass.
14 . The system of claim 8 , wherein the one or more processors are further configured to:
transmit the notification for display on an electronic device, wherein the notification comprises one or more of a score, a figure, a graph, a symbol, or a color field to alert the user of the level of risk exposure, as predicted, for the other activity.
15 . One or more non-transitory computer-readable media comprising computing instructions that, when executed on one or more processors, cause the one or more processors to perform operations comprising:
acquiring, via a sleep detecting device associated with a user, a set of user data including data collected by a sensor disposed in the sleep detecting device;
predicting, by at least a trained ML model, a level of risk exposure for an other activity for the user, wherein input for the trained ML model is based on at least the set of user data, wherein the trained ML model is trained utilizing one or more sets of first data indicative of one or more sleep patterns collected by the sleep detecting device and one or more sets of second data indicative of the other activity to identify one or more relationships between the one or more sleep patterns and the level of risk exposure for the other activity; and
generating a notification to alert the user of the level of risk exposure, as predicted, for the other activity.
16 . The one or more non-transitory computer-readable media of claim 15 , wherein:
the sleep detecting device is configured to interact with one or more of a wearable device, a mobile device, or a vehicle interface.
17 . The one or more non-transitory computer-readable media of claim 15 , wherein the operations further comprise:
transferring energy to the sleep detecting device via a charging device while the user is wearing the sleep detecting device.
18 . The one or more non-transitory computer-readable media of claim 15 , wherein:
the one or more sets of first data include one or more of a heart rate variability of the user, a blood pressure of the user, a body temperature of the user, a skin conductance of the user, a skin perfusion of the user, a sweat amount of the user, a sweat concentration of a substance of the user, or a body movement measurement of the user; and
a date and time stamp associated with the one or more sets of first data.
19 . The one or more non-transitory computer-readable media of claim 15 , wherein:
the one or more sets of second data include one or more of an acceleration measurement, a braking measurement, a swerving measurement, a proximity measurement to another vehicle, an adherence to a road marker measurement, or a speed measurement.
20 . The one or more non-transitory computer-readable media of claim 15 , wherein:
one or more other detecting devices collect the one or more sets of second data indicative of the other activity; and
the one or more other detecting devices include one or more of a speedometer, an accelerometer, a camera, an image sensor, a laser sensor, a RADAR sensor, an infrared sensor, a GPS receiver, or a compass.