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US 12,230,366 B2Granted
System and method for fleet driver biometric tracking
AssigneeBlyncsync Technologies, LLC
InventorsAustin Green, Steven Kastelic
Life of this patent
Estimating the term…
Abstract
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.
Claims16
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.
12 more elements
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.
15 more elements
Dependencies are inferred from the claim text.