System and method for gathering and analyzing biometric user feedback for use in social media and advertising applications

Abstract

Systems and methods for measuring biologically and behaviorally based responses to social media, locations, or experiences and providing instant and continuous feedback in response thereto are disclosed. An example system includes a first sensor to determine an emotional response of a user exposed to a social media application, a second sensor to determine a current activity of the user, and a third sensor to determine an environment of the user. The example system also establishes a priority schedule based on the emotional response, the current activity, and the environment. The system also correlates, based on the priority schedule, an advertisement with at least one of the emotional response, activity, or the environment. In addition, the example system presents the advertisement based on the priority schedule and the correlation of the advertisement with the at least one of the activity, the environment, or the emotional response.

1 . (canceled) 
     
     
         2 . A system comprising:
 a sensor to collect biometric data from a user;   a holder to secure the sensor to the user;   a memory; and   a processor to:
 determine a first activity of the user, the first activity related to use of an application on a mobile device; 
 determine a second activity of the user, the second activity related to a motion data of the user; 
 determine an emotional state of the user based on the biometric data; and 
 provide a media content recommendation based on the emotional state of the user and at least one of the first activity or the second activity. 
   
     
     
         3 . The system of  claim 2 , wherein the processor is to prioritize one of the first activity or the second activity over the other of the first activity or the second activity based on a machine learning model. 
     
     
         4 . The system of  claim 3 , wherein the machine learning model includes prioritization of the first activity or the second activity based on a prior use of the mobile device and a prioritization scale that provides a first weight to a first activity type and a second weight to a second activity type, the second activity type different from the first activity type. 
     
     
         5 . The system of  claim 2 , wherein the application is a first application, and the processor is to operate a second application to train the machine learning model to prioritize the first activity or the second activity. 
     
     
         6 . The system of  claim 2 , wherein the sensor is a first sensor to collect a first biometric data, the biometric data including one of a galvanic skin response, a heart rate, a skin temperature, an eye movement, an electroencephalogram, an electromyogram, or a pupil dilation, the system further including a second sensor to collect a second biometric data, the second biometric data different than the first biometric data, the processor to determine the emotional state of the user based on the first and second biometric data. 
     
     
         7 . The system of  claim 2 , wherein the holder includes at least one of a wrist band, an arm band, or a headpiece. 
     
     
         8 . The system of  claim 2 , wherein the motion data includes velocity and the processor is to provide the media content recommendation based on the velocity. 
     
     
         9 . The system of  claim 8 , wherein the media content recommendation includes a first level of activity for a first velocity and a second level of activity for a second velocity, the first level of activity lower than the second level of activity and the first velocity lower than the second velocity. 
     
     
         10 . The system of  claim 2 , wherein the motion data includes a direction, and the processor is to:
 determine a location of the user based on the direction; and   determine the media content recommendation based on the location.   
     
     
         11 . A tangible computer readable storage medium comprising instructions that, when executed, cause at least one processor to at least:
 determine a first activity of a user, the first activity related to use of an application on a mobile device;   determine a second activity of the user, the second activity related to a motion data of the user;   determine an emotional state of the user based on biometric data; and   provide a media content recommendation based on the emotional state of the user and at least one of the first activity or the second activity.   
     
     
         12 . The storage medium of  claim 11 , wherein the instructions cause the processor to prioritize one of the first activity or the second activity over the other of the first activity or the second activity based on a machine learning model. 
     
     
         13 . The storage medium of  claim 12 , wherein the machine learning model includes prioritization of the first activity or the second activity based on a prior use of the mobile device and a prioritization scale that provides a first weight to a first activity type and a second weight to a second activity type, the second activity type different from the first activity type. 
     
     
         14 . The storage medium of  claim 11 , wherein the motion data includes velocity and the instructions cause the processor to provide the media content recommendation based on the velocity. 
     
     
         15 . The storage medium of  claim 14 , wherein the media content recommendation includes a first level of activity for a first velocity and a second level of activity for a second velocity, the first level of activity lower than the second level of activity and the first velocity lower than the second velocity. 
     
     
         16 . The storage medium of  claim 11 , wherein the motion data include a direction, and the instructions cause the processor to:
 determine a location of the user based on the direction; and   determine the media content recommendation based on the location.   
     
     
         17 . A method comprising:
 determining, by executing instructions with a processor, a first activity of a user, the first activity related to use of an application on a mobile device;   determining, by executing instructions with the processor, a second activity of the user, the second activity related to a motion of the user;   determining, by executing instructions with the processor, an emotional state of the user based on a biometric data; and   providing, by executing instructions with the processor, a media content recommendation based on the emotional state of the user and at least one of the first activity or the second activity.   
     
     
         18 . The method of  claim 17 , wherein the application is a first application, the method further including operating a second application to train a machine learning model to prioritize one of the first activity or the second activity over the other of the first activity or the second activity based on a prior use of the mobile device and a prioritization scale that provides a first weight to a first activity type and a second weight to a second activity type, the second activity type different from the first activity type. 
     
     
         19 . The method of  claim 17 , wherein the motion data includes velocity, the method further including providing the media content recommendation based on the velocity, the media content recommendation including a first level of activity for a first velocity and a second level of activity for a second velocity, the first level of activity lower than the second level of activity and the first velocity lower than the second velocity. 
     
     
         20 . The method of  claim 17 , wherein the motion data include a direction, the method further including:
 determining a location of the user based on the direction; and   determining the media content recommendation based on the location.