Methods and apparatus for monitoring alertness of an individual utilizing a wearable device and providing notification

Abstract

Methods and apparatus for monitoring fatigue and notifying an individual are described. The individual may be an operator of a vehicle, equipment, or machine, a student, or other person that may experience fatigue. Motion of the individual is monitored to detect a predescribed motion in response to a stimulus to first determine a base responsiveness profile. Afterwards, a current responsiveness profile is determined based on a prescribed motion in response to a stimulus, and if the current responsiveness profile exceeds a predetermined threshold of the base responsiveness profile, a notification is issued to the individual and, optionally, another person such as an employer, teacher, or parent.

1 . A system for monitoring fatigue and notifying a user, the system comprising:
 a wearable apparatus comprising:
 a sensor configured to measure biomarkers of the user; 
 a communication module; and 
 a support configured to support the sensor; and 
   a remote device comprising:
 memory; 
 a processor communicatively coupled to the sensor, the processor configured to:
 obtain, from the communication module, biomarker measurements from the sensor; 
 generate a personal circadian rhythm for the user, and a position of the user along that circadian rhythm, using the measured biomarkers; 
 identify a baseline risk level for the user by assessing the personal circadian rhythm of the user and the position of the user along that personal circadian rhythm; 
 determine a sleep drive of the user based upon continual or periodic sensor measurements, wherein sleep and wakefulness cycles of the user are derived from actigraphy data obtained from the sensor; and 
 determine that the baseline risk level is in a predetermined range. 
 
   
     
     
         2 . The system of  claim 1 , wherein the processor is further configured to:
 generate a mask event of the personal circadian rhythm based upon changes in body position or activity of the user; and   remove the mask event from a personal circadian rhythm estimate by scaling data from the personal circadian rhythm to correct for body position as measured by the sensor and averaging personal circadian rhythm data in a testing period.   
     
     
         3 . The system of  claim 2 , wherein the processor is further configured to:
 scale the personal circadian rhythm data to generate corrected data for an activity of the user during the testing period as measured by the sensor; and   generate a more accurate estimate of the personal circadian rhythm based on the corrected data.   
     
     
         4 . The system of  claim 1 , wherein the processor is further configured to generate the personal circadian rhythm, and the position of the user along that personal circadian rhythm, based upon a skin temperature of the user as measured by the sensor. 
     
     
         5 . The system of  claim 4 , wherein the processor is further configured to generate the personal circadian rhythm by correlating a distal skin temperature of the user to a core body temperature of the user. 
     
     
         6 . A method for monitoring fatigue and notifying a user, comprising:
 affixing on the user an apparatus comprising a sensor and a communication module, wherein the sensor is in communication, via the communication module, with a processor configured to detect and monitor biomarkers of the user;   generating, by the processor, an estimated personal circadian rhythm for the user based upon measured biomarkers;   determining, by the processor, a dynamic risk level for the user based on the estimated personal circadian rhythm of the user, and a position of the user along that estimated personal circadian rhythm;   determining, by the processor, a sleep drive of the user based upon continual or periodic sensor measurements, wherein sleep and wakefulness cycles of the user are derived from actigraphy data obtained from the sensor; and   determine that an identified risk level is in a predetermined range.   
     
     
         7 . The method of  claim 6 , further comprising:
 generating a mask event of the estimated personal circadian rhythm based upon changes in body position or activity of the user; and   removing the mask event from the estimated personal circadian rhythm by scaling data from the circadian rhythm to correct for body position as measured by the sensor and averaging the circadian rhythm data in a testing period.   
     
     
         8 . The method of  claim 7 , further comprising:
 scaling the circadian rhythm data to generate corrected data for an activity of the user during the testing period as measured by the sensor; and   generating a more accurate estimate of the personal circadian rhythm based on the corrected data.   
     
     
         9 . The method of  claim 6 , wherein one of the biomarkers is skin temperature. 
     
     
         10 . The method of  claim 6 , wherein biomarker measurements relied upon to determine the dynamic risk level are made at the sensor. 
     
     
         11 . The method of  claim 10 , wherein the sensor is located on a distal portion of the user. 
     
     
         12 . A wearable device for monitoring fatigue comprising:
 a sensor configured to measure biomarkers and actigraphy data of a user, wherein the sensor is communicatively coupled to:
 a remote device comprising memory and a processor; and 
   a communication module configured to:
 output, to the remote device, biomarker measurements from the sensor; 
 receive, from the remote device:
 personal circadian rhythm for the user, and a position of the user along that circadian rhythm, using the measured biomarkers; 
 a baseline risk level for the user by assessing the personal circadian rhythm of the user and the position of the user along that personal circadian rhythm; and 
 a sleep drive of the user based upon continual or periodic sensor measurements, wherein sleep and wakefulness cycles of the user are derived from actigraphy data obtained from the sensor; and 
 
 receive from the processor a determination that the baseline risk level is in a predetermined range; and 
   a support configured to support the sensor and the communication module.   
     
     
         13 . The wearable device of  claim 12 , wherein the communication module is further configured to:
 generate a mask event of the personal circadian rhythm based upon changes in body position or activity of the user; and   remove the mask event from a personal circadian rhythm estimate by scaling data from the personal circadian rhythm to correct for body position as measured by the sensor and averaging personal circadian rhythm data in a testing period.   
     
     
         14 . The wearable device of  claim 13 , wherein the communication module is further configured to:
 scale the personal circadian rhythm data to generate corrected data for an activity of the user during the testing period as measured by the sensor; and   generate a more accurate estimate of the personal circadian rhythm based on the corrected data.   
     
     
         15 . The system of  claim 12 , wherein the communication module is further configured to generate the personal circadian rhythm, and the position of the user along that personal circadian rhythm, based upon a skin temperature of the user as measured by the sensor. 
     
     
         16 . The system of  claim 15 , wherein the communication module is further configured to generate the personal circadian rhythm by correlating a distal skin temperature of the user to a core body temperature of the user. 
     
     
         17 . A method for monitoring fatigue, comprising:
 affixing on a user a wearable device comprising a sensor and a communication module;   measuring, by the sensor, biomarkers and actigraphy data from the user;   outputting, by the communication module, the biomarkers and actigraphy data from the wearable device to a remote device;   receiving, at the sensor, personal circadian rhythm for the user, and a position of the user along that circadian rhythm, using the measured biomarkers;   receiving, at the communication module, a baseline risk level for the user by assessing the personal circadian rhythm of the user and the position of the user along that personal circadian rhythm; and   receiving, at the sensor, a sleep drive of the user based upon continual or periodic sensor measurements, wherein sleep and wakefulness cycles of the user are derived from actigraphy data obtained from the sensor; and   receiving at the communication module, from (i) the remote device or (ii) a third party device that is remote with respect to the wearable device and the remote device, a determination that an identified risk level is in a predetermined range.   
     
     
         18 . The method of  claim 17 , further comprising:
 generating a mask event of the estimated personal circadian rhythm based upon changes in body position or activity of the user; and   removing the mask event from the estimated personal circadian rhythm by scaling data from the circadian rhythm to correct for body position as measured by the sensor and averaging the circadian rhythm data in a testing period.   
     
     
         19 . The method of  claim 18 , further comprising:
 scaling the circadian rhythm data to generate corrected data for an activity of the user during the testing period as measured by the sensor; and   generating a more accurate estimate of the personal circadian rhythm based on the corrected data.   
     
     
         20 . The method of  claim 17 , wherein one of the biomarkers is skin temperature. 
     
     
         21 . The method of  claim 17 , wherein biomarker measurements relied upon to determine the dynamic risk level are made at the sensor. 
     
     
         22 . The method of  claim 21 , wherein the sensor is located on a distal portion of the user. 
     
     
         23 . A computing device for monitoring fatigue, the computing device comprising:
 memory;   a communication module; and   a processor communicatively coupled via the communication module to a user device worn by a user, the processor configured to:
 obtain, by the communication module, biomarker measurements about the user from a sensor in the user device; 
 generate a personal circadian rhythm for the user, and a position of the user along that circadian rhythm, using the measured biomarkers; 
 identify a baseline risk level for the user by assessing the personal circadian rhythm of the user and the position of the user along that personal circadian rhythm; 
 determine a sleep drive of the user based upon continual or periodic sensor measurements, wherein sleep and wakefulness cycles of the user are derived from actigraphy data obtained from the sensor; and 
 output, by the communication module, that the baseline risk level is in a predetermined range. 
   
     
     
         24 . The computing device of  claim 23 , wherein the processor is further configured to:
 generate a mask event of the personal circadian rhythm based upon changes in body position or activity of the user; and   remove the mask event from a personal circadian rhythm estimate by scaling data from the personal circadian rhythm to correct for body position as measured by the sensor and averaging personal circadian rhythm data in a testing period.   
     
     
         25 . The computing device of  claim 24 , wherein the processor is further configured to:
 scale the personal circadian rhythm data to generate corrected data for an activity of the user during the testing period as measured by the sensor; and   generate a more accurate estimate of the personal circadian rhythm based on the corrected data.   
     
     
         26 . The system of  claim 23 , wherein the processor is further configured to generate the personal circadian rhythm, and the position of the user along that personal circadian rhythm, based upon a skin temperature of the user as measured by the sensor. 
     
     
         27 . The system of  claim 26 , wherein the processor is further configured to generate the personal circadian rhythm by correlating a distal skin temperature of the user to a core body temperature of the user. 
     
     
         28 . A method for monitoring fatigue, comprising:
 receiving at a communication module in a first device, from a wearable device worn by a user and communicatively coupled to the first device, biomarkers and actigraphy data from the user;   generating, by a processor in the first device, an estimated personal circadian rhythm for the user based upon measured biomarkers;   determining, by the processor, a dynamic risk level for the user based on the estimated personal circadian rhythm of the user, and a position of the user along that estimated personal circadian rhythm;   determining a sleep drive of the user based upon continual or periodic measurements from a sensor, wherein sleep and wakefulness cycles of the user are derived from actigraphy data obtained from the sensor; and   outputting, from the communication module that an identified risk level is in a predetermined range.   
     
     
         29 . The method of  claim 28 , further comprising:
 generating a mask event of the estimated personal circadian rhythm based upon changes in body position or activity of the user; and   removing the mask event from the estimated personal circadian rhythm by scaling data from the circadian rhythm to correct for body position as measured by the sensor and averaging the circadian rhythm data in a testing period.   
     
     
         30 . The method of  claim 29 , further comprising:
 scaling the circadian rhythm data to generate corrected data for an activity of the user during the testing period as measured by the sensor; and   generating a more accurate estimate of the personal circadian rhythm based on the corrected data.   
     
     
         31 . The method of  claim 28 , wherein one of the biomarkers is skin temperature. 
     
     
         32 . The method of  claim 28 , wherein biomarker measurements relied upon to determine the dynamic risk level are made at the sensor. 
     
     
         33 . The method of  claim 32 , wherein the sensor is located on a distal portion of the user.