System and method for fleet driver biometric tracking
A method for employee biometric tracking is provided. The method comprises providing to a user a plurality of wearable devices capable of being connected to the user, establishing a wireless connection between the plurality of wearable devices and a mobile device, collecting by the plurality of wearable devices a plurality of biometric data from the user, receiving by an application stored on the mobile device the plurality of biometric data, inputting into a predictive engine biometric data selected from the plurality of biometric data, determining by the predictive engine in response to the biometric data whether the user is at, or soon will be at, an alert level, creating an alert signal, and displaying the alert signal to the user.
1. A method for biometric tracking, comprising:
receiving, using one or more wearable devices, a plurality of biometric data including blink rate data, head acceleration data, and sleep data, wherein the sleep data includes REM sleep data;
creating and updating over time an electronic log, wherein the electronic log includes a plurality of states of a user each mapped to one or more time periods and each mapped to one or more of the plurality of biometric data;
inputting, into a predictive engine, biometric data selected from the plurality of biometric data, wherein the predictive engine is a machine learning model;
training the predictive engine using the biometric data;
retraining the predictive engine to provide for an adjusted a predicted behavior pattern of the predictive engine based on the sleep data received over time, including adjusting an under-prediction or an over-prediction by gradually increasing or decreasing a predicted number of awake hours at periodic intervals, including:
adjusting the under-prediction by the predictive engine by gradually increasing the predicted number of awake hours at the periodic intervals, wherein the under-prediction indicates that the predicted number of awake hours for the user is predicted based on tracked REM sleep hours for the user and is predicted less than a number of actual awake hours of the user;
adjusting the over-prediction by the predictive engine by gradually decreasing the predicted number of awake hours at the periodic intervals, wherein the over-prediction indicates that the predicted number of awake hours for the user is predicted based on the tracked REM sleep hours for the user and is predicted greater than the number of actual awake hours of the user; and
modifying weights of the predictive engine at the periodic intervals according to the adjusted predicted behavior pattern via backpropagation of errors through the predictive engine; and
outputting the adjusted predicted behavior pattern using the predictive engine and the modified weights, including:
providing biometric inputs to the predictive engine;
manipulating the inputs by the modified weights; and
applying an activation function to the manipulated inputs.
2. The method of claim 1 , further comprising updating fatigue patterns based on the retraining of the predictive engine.
3. The method of claim 2 , further comprising:
determining, by the predictive engine in response to the biometric data and based on the fatigue patterns, whether the user is at, or soon will be at, a fatigue alert level;
creating an alert signal;
displaying the alert signal to the user and to a remote management system;
storing, by the remote management system, an alert event, wherein the alert event includes information comprising the biometric data that triggered the alert signal; and
updating the fatigue patterns of the predictive engine for the user based on the alert event.
4. The method of claim 3 , wherein displaying the alert signal to the user and to the remote management system further includes simultaneously displaying the alert signal to the remote management system.
5. The method of claim 3 , further comprising storing, in a local application, the alert event.
6. The method of claim 3 , wherein the plurality of biometric data includes at least one biometric data type, and wherein the fatigue alert level is a threshold value associated with the at least one biometric data type.
7. The method of claim 3 , wherein training the predictive engine uses the biometric data that triggered the alert signal and the fatigue patterns for the user, wherein the predictive engine is trained on data specific to the user.
8. The method of claim 1 , wherein the plurality of states includes:
a sleep state corresponding to when the user is actively sleeping,
an on state corresponding to when the user is working but not driving,
an off state in which the user is not working, and
a drive state corresponding to when the user is driving or performing another task.
9. A system for biometric tracking, comprising:
at least one processor; and
a memory coupled to the at least one processor, the memory including computer executable instructions that cause the at least one processor to:
receive, using one or more wearable devices, a plurality of biometric data, wherein the plurality of biometric data includes blink rate data, head acceleration data, and sleep data, and wherein the sleep data includes REM sleep data;
create and update over time an electronic log, wherein the electronic log includes a plurality of states of a user each mapped to one or more time periods and each mapped to one or more of the plurality of biometric data;
input, into a predictive engine, biometric data selected from the plurality of biometric data, wherein the predictive engine is a machine learning model;
train the predictive engine using the biometric data;
retraining the predictive engine to provide for an adjusted predicted behavior pattern of the predictive engine based on the sleep data received over time, including adjusting an under-prediction or an over-prediction by gradually increasing or decreasing a predicted number of awake hours at periodic intervals, including:
adjust the under-prediction by the predictive engine by gradually increasing the predicted number of awake hours at the periodic intervals, wherein the under-prediction indicates that the predicted number of awake hours for the user is predicted based on tracked REM sleep hours for the user and is predicted less than a number of actual awake hours of the user;
adjust the over-prediction by the predictive engine by gradually decreasing the predicted number of awake hours at the periodic intervals, wherein the over-prediction indicates that the predicted number of awake hours for the user is predicted based on tracked REM sleep hours for the user and is predicted greater than a number of actual awake hours of the user; and
modify weights of the predictive engine at the periodic intervals according to the adjusted predicted behavior pattern via backpropagation of errors through the predictive engine; and
output the adjusted predicted behavior pattern using the predictive engine and the modified weights, including:
provide biometric inputs to the predictive engine;
manipulate the inputs by the modified weights; and
apply an activation function to the manipulated inputs.
10. The system of claim 9 , wherein the memory further includes computer executable instructions that cause the at least one processor to update fatigue patterns based on the retraining of the predictive engine.
11. The system of claim 10 , wherein the memory further includes computer executable instructions that cause the at least one processor to:
determine, by the predictive engine in response to the biometric data and based on the fatigue patterns, whether the user is at, or soon will be at, a fatigue alert level;
create an alert signal;
display the alert signal to the user and to a remote management system;
store, at the remote management system, an alert event, wherein the alert event includes information comprising the biometric data that triggered the alert signal; and
update the fatigue patterns of the predictive engine for the user based on the alert event.
12. The system of claim 11 , wherein displaying the alert signal to the user and to the remote management system further includes simultaneously displaying the alert signal to the remote management system.
13. The system of claim 11 , wherein the memory further includes computer executable instructions that cause the at least one processor to store, in a local application, the alert event.
14. The system of claim 11 , wherein the memory further includes computer executable instructions that cause the at least one processor to use the biometric data that triggered the alert signal and the fatigue patterns for the user during training of the predictive engine, wherein the predictive engine is trained on data specific to the user.
15. The system of claim 11 , wherein the plurality of biometric data includes at least one biometric data type and wherein the fatigue alert level is a threshold value associated with the at least one biometric data type.
16. The system of claim 9 , wherein the plurality of states includes:
a sleep state corresponding to when the user is actively sleeping,
an on state corresponding to when the user is working but not driving,
an off state in which the user is not working, and
a drive state corresponding to when the user is driving or performing another task.