METHOD, APPARATUS, AND COMPUTER PROGRAM PRODUCT FOR MONITORING HEALTH, FITNESS, OPERATION, OR PERFORMANCE OF INDIVIDUALS

• US 20140364973A1
• Filed: 07/15/2013
• Published: 12/11/2014
• Est. Priority Date: 06/06/2013
• Status: Abandoned Application

• Current Assignee: Zebra Technologies Corporation
• Original Assignee: ZIH Corp. (Zebra Technologies Corporation)
• Inventors: O'Hagan, James J., Wohl, Michael A., Strobel, Wolfgang
• Application Number:
  US13/942,139
• Publication Number:
  US 20140364973A1
• Time in Patent Office: Days
• US Class Current: 700/91
• CPC Class Codes: A41D 1/002 adapted to accommodate electronic equipment patients' garments for medical monitoring A41D13/1281; luminous ornaments A41D27/085
• CPC Class Codes: A41D 1/005 with embedded cable or connector
• CPC Class Codes: A41D 1/04 Vests, jerseys, sweaters or the like
• CPC Class Codes: A41D 2600/10 for sport activities
• CPC Class Codes: A42B 3/30 Mounting radio sets or communication systems
• CPC Class Codes: A63B 2024/0025 Tracking the path or location of one or more users, e.g. players of a game
• CPC Class Codes: A63B 2024/0028 Tracking the path of an object, e.g. a ball inside a soccer pitch
• CPC Class Codes: A63B 2024/0056 for statistical or strategic analysis
• CPC Class Codes: A63B 2220/12 Absolute positions, e.g. by using GPS
• CPC Class Codes: A63B 2220/40 Acceleration
• CPC Class Codes: A63B 2220/836 Sensors arranged on the body of the user
• CPC Class Codes: A63B 2225/50 Wireless data transmission, e.g. by radio transmitters or telemetry
• CPC Class Codes: A63B 2225/54 Transponders, e.g. RFID
• CPC Class Codes: A63B 24/00 Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
• CPC Class Codes: A63B 24/0021 Tracking a path or terminating locations
• CPC Class Codes: A63B 71/0686 Timers, rhythm indicators or pacing apparatus using electric or electronic means
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9537 Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
• CPC Class Codes: G06F 16/955 using information identifiers, e.g. uniform resource locators [URL]
• CPC Class Codes: G06F 16/9554 : by using bar codes
• CPC Class Codes: G06K 7/10227 : loading programming parameters or programs into the interrogator, e.g. for configuring the interrogator
• CPC Class Codes: G06K 7/10297 : arrangements for handling protocols designed for non-contact record carriers such as RFIDs NFCs, e.g. ISO/IEC 14443 and 18092 cryptographic protocols H04L9/00; network security protocols H04L63/00; real-time communication protocols in data switching networks H04L65/00; network protocols for data switching network services H04L67/00
• CPC Class Codes: G06K 7/10306 : ultra wide band
• CPC Class Codes: G06K 7/10366 : the interrogation device being adapted for miscellaneous applications
• CPC Class Codes: G06N 5/02 : Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 7/01 : Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 90/00 : Systems or methods specially adapted for administrative, commercial, financial, managerial or supervisory purposes, not involving significant data processing
• CPC Class Codes: G06V 40/23 : Recognition of whole body movements, e.g. for sport training
• CPC Class Codes: G08C 17/02 : using a radio link
• CPC Class Codes: G09B 19/0038 : Sports
• CPC Class Codes: G16H 40/67 : for remote operation
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders
• CPC Class Codes: H04B 1/1036 : with automatic suppression of narrow band noise or interference, e.g. by using tuneable notch filters H04B1/123 takes precedence; filter circuits H03H
• CPC Class Codes: H04B 1/7097 : Interference-related aspects
• CPC Class Codes: H04B 1/71635 : Transmitter aspects H04B1/7174 takes precedence
• CPC Class Codes: H04B 1/71637 : Receiver aspects H04B1/7183 takes precedence
• CPC Class Codes: H04B 1/719 : Interference-related aspects
• CPC Class Codes: H04L 43/04 : Processing captured monitoring data, e.g. for logfile generation
• CPC Class Codes: H04Q 9/00 : Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
• CPC Class Codes: H04W 4/02 : Services making use of location information
• CPC Class Codes: H04W 4/029 : Location-based management or tracking services

Abstract

Provided herein are systems, methods and computer readable media for monitoring the health and fitness of an individual. An example method comprises correlating a tag and a sensor to the individual, receiving tag derived data indicative of a location for the individual, and receiving sensor derived data indicative of at least one of a health, a fitness, an operation level, or a performance level for the individual. The method further comprises comparing the tag location data of the tag derived data to individual dynamics/kinetics models and comparing the sensor derived data to at least one of health models, fitness models, operation level models, or performance level models. A HFOP status is then determined for the individual based on the comparing the tag location data to individual dynamics/kinetics models and on the comparing the sensor derived data to at least one of health models, fitness models, operation level models, or performance level models.

Claims

1. A method for monitoring an individual, the method comprising:

receiving tag derived data comprising tag location data and blink data, wherein the tag location data is determined based at least in part on the blink data;

receiving sensor derived data indicative of at least one of a health, a fitness, an operation level, or a performance level for the individual;

comparing the tag location data to individual dynamics/kinetics models;

comparing the sensor derived data to at least one of health models, fitness models, operation level models, or performance level models; and

determining a health, fitness, operation and performance (HFOP) status for the individual based on the comparing the tag location data to individual dynamics/kinetics models and on the comparing the sensor derived data to at least one of health models, fitness models, operation level models, or performance level models.

2. The method according to claim 1 further comprising:

determining whether the HFOP status satisfies a threshold value and generating, in an instance in which the HFOP status does satisfy the threshold value, an alert.

3. The method according to claim 1, wherein the blink data is generated by one or more tags comprising an ultra-wideband (UWB) transmitter.

4. The method according to claim 3, wherein the UWB transmitter is configured to transmit a plurality of time of arrival (TOA) timing pulses.

5. The method according to claim 3, wherein the UWB transmitter is configured to transmit a tag data packet comprising 112 bits.

6. The method according to claim 1, wherein the tag derived data further comprises a tag UID and a tag-individual correlator.

7. The method according to claim 1, wherein the tag derived data further comprises tag data, a tag UID, a tag-sensor correlator, and a sensor information packet.

8. The method according to claim 1, wherein the sensor derived data comprises a sensor UID and additional stored sensor data.

9. The method according to claim 1, wherein the sensor derived data comprises a sensor UID, additional stored sensor data, a sensor-individual correlator, and environmental measurements.

10. The method according to claim 1, wherein the sensor derived data comprises a position calculation determined by a triangulation positioner.

11. The method according to claim 1, wherein the sensor derived data comprises position information read from a proximity label.

12. The method according to claim 1, wherein the sensor derived data comprises associated sensor data generated by a sensor receiver upon receiving sensor information packets from first and second sensors.

13. The method according to claim 1, wherein at least part of the sensor derived data was transmitted from a location tag as part of a tag data packet.

14. The method according to claim 1, wherein the sensor derived data comprises environmental measurements generated by one or more sensors comprising at least one of an accelerometer, a diagnostic device, a triangulation positioner, or a proximity positioner.

15. The method according to claim 1, wherein the individual is a human being and the sensor derived data is generated from one or more sensors comprising at least one of a blood pressure sensor, a heart rate sensor, a body temperature sensor, an eye dilation sensor, a hydration sensor, a blood chemistry sensor, an ambient temperature sensor, a humidity sensor, or a barometric pressure sensor.

16. The method according to claim 1, wherein the individual is an animal and the sensor derived data is generated from one or more sensors comprising at least one of a blood pressure sensor, a heart rate sensor, a body temperature sensor, an eye dilation sensor, a hydration sensor, a blood chemistry sensor, an ambient temperature sensor, a humidity sensor, or a barometric pressure sensor.

17. The method according to claim 1, wherein the individual is a machine and the sensor derived data is generated from one or more sensor comprising at least one a throttle position sensor, brake position sensor, steering position sensor, axle, wheel or drive shaft sensor, rotary position sensor, crankshaft position sensor, engine coolant temperature sensor, water temperature sensor, oil temperature sensor, fuel gauge sensor, oil gauge sensor, suspension travel sensor, accelerometer, or pressure sensor.

18. The method according to claim 1, wherein the individual dynamics/kinetics models comprise location history data for the individual.

19. The method according to claim 1, wherein the at least one of health models, fitness models, operation level models, or performance level models comprise a respective one of a health history data, a fitness history data, an operation level history data, or a performance level history data.

20. The method according to claim 2 further comprising:

comparing the sensor derived data to pre-defined thresholds, and determining whether the sensor-derived data satisfies the pre-defined thresholds.

21. The method according to claim 1, wherein the individual is a human being and the tag location data is indicative of a position of at least one of a head, shoulder, a torso, an elbow, a hand, a hip, a knee, and a foot.

22. The method according to claim 1, wherein the tag location data comprises tag location data for each of two or more tags, each indicative of a position for a different part of the individual.

23. The method according to claim 22, wherein the tag location data is indicative of foot position of each of two feet for the individual, and wherein the method further comprises determining a gait based on the tag location data.

24. The method according to claim 22, wherein the individual is a human being and the tag location data is indicative of a position of one or more of:

each of two hands, each of two feet, each of two knees, each of two hips, each of two elbows, and each of two shoulders.

25. The method according to claim 1, wherein the sensor derived data comprises a representation of sound made by the individual.

26. The method according to claim 1, wherein comparing the tag location data to individual dynamics/kinetics models comprises selecting a dynamics/kinetics model for comparing, and wherein the selecting the dynamics/kinetics model is based on identity information.

27. The method according to claim 1, wherein comparing the tag location data to individual dynamics/kinetics models comprises selecting a dynamics/kinetics model for comparing, and wherein the selecting the dynamics/kinetics model is based on zone data.

28. The method according to claim 1, wherein comparing the tag location data to individual dynamics/kinetics models comprises selecting a dynamics/kinetics model for comparing, and wherein the selecting the dynamics/kinetics model is based on role data.

29. The method according to claim 1, wherein comparing the tag location data to individual dynamics/kinetics models comprises selecting a dynamics/kinetics model for comparing, and wherein the selecting the dynamics/kinetics model is based on identity information and role data.

30. A computer program product for monitoring an individual comprising at least one computer-readable storage medium having computer-executable program code instructions stored therein, the computer-executable program code instructions comprising program code instructions for:

receiving tag derived data comprising tag location data and blink data, wherein the tag location data is generated based at least in part based on the blink data;

receiving sensor derived data indicative of at least one of a health, a fitness, an operation level, or a performance level for the individual;

comparing the tag location data to individual dynamics/kinetics models;

comparing the sensor derived data to at least one of health models, fitness models, operation level models, or performance level models; and

determining a health, fitness, operation and performance (HFOP) status based on the comparing the tag location data to individual dynamics/kinetics models and on the comparing the sensor derived data to at least one of health models, fitness models, operation level models, or performance level models.

31. The computer program product according to claim 30, wherein the computer-executable program code portions further comprise program code instructions for:

determining whether the HFOP status satisfies a threshold value and generating, in an instance in which the HFOP status does satisfy the threshold value, an alert.

32. The computer program product according to claim 30, wherein the tag derived data is generated by a receiver configured to receive tag signals generated by an ultra-wideband (UWB) transmitter.

33. The computer program product according to claim 30, wherein the sensor derived data comprises environmental measurements generated by one or more sensors comprising at least one of an accelerometer, a diagnostic device, a triangulation positioner, or a proximity positioner.

34. The computer program product according to claim 30, wherein the sensor derived data comprises environmental measurements generated by one or more sensors comprising at least one of a blood pressure sensor, a heart rate sensor, a body temperature sensor, an eye dilation sensor, a hydration sensor, a blood chemistry sensor, an ambient temperature sensor, a humidity sensor, or a barometric pressure sensor.

35. The computer program product according to claim 30, wherein the sensor derived data comprises environmental measurements generated by one or more sensors comprising at least one a throttle position sensor, brake position sensor, steering position sensor, axle, wheel or drive shaft sensor, rotary position sensor, crankshaft position sensor, engine coolant temperature sensor, water temperature sensor, oil temperature sensor, fuel gauge sensor, oil gauge sensor, suspension travel sensor, accelerometer, or pressure sensor.

36. The computer program product according to claim 30, wherein the individual dynamics/kinetics models comprise location history data for the individual.

37. The computer program product according to claim 30, wherein the at least one of health models, fitness models, operation level models, or performance level models comprise a respective one of a health history data, a fitness history data, an operation level history data, or a performance level history data.

38. The computer program product according to claim 30, wherein the computer-executable program code portions further comprise program code instructions for

comparing the sensor derived data to pre-defined thresholds, and generating an alert in an instance in which particular sensor derived data satisfies an associated threshold.

39. The computer program product according to claim 30, wherein the tag location data comprises location data for each of two or more tags, each indicative of a position for a different part of the individual.

40. The computer program product according to claim 30, wherein the tag-derived data is indicative of a human being and the tag location data is indicative of a position of one or more of:

each of two hands, each of two feet, each of two knees, each of two hips, each of two elbows, and each of two shoulders.

41. An apparatus comprising a processor and a memory having computer code stored therein, the computer code configured, when executed by the processor, to cause the apparatus to:

receive tag derived data comprising tag location data and blink data, wherein the tag location data is determined based at least in part on the blink data;

receive sensor derived data indicative of at least one of a health, a fitness, an operation level, or a performance level for an individual;

compare the tag location data to individual dynamics/kinetics models;

compare the sensor derived data to at least one of health models, fitness models, operation level models, or performance level models; and

determine a health, fitness, operation and performance (HFOP) status for the individual based on the comparing the tag location data to individual dynamics/kinetics models and on the comparing the sensor derived data to at least one of health models, fitness models, operation level models, or performance level models.

42. The apparatus of claim 41, wherein the received tag derived data further comprises a tag UID and a tag-individual correlator.

43. The apparatus of claim 41, wherein the received tag derived data further comprises tag data, a tag UID, a tag-sensor correlator, and a sensor information packet.

44. The apparatus of claim 41, wherein the sensor derived data comprises a sensor UID and additional stored sensor data.

45. The apparatus of claim 41, wherein the sensor derived data comprises a sensor UID, additional stored sensor data, a sensor-individual correlator, and environmental measurements.

46. The apparatus of claim 41, wherein the sensor derived data comprises a position calculation determined by a triangulation positioner.

47. The apparatus of claim 41, wherein the sensor derived data comprises position information read from a proximity label.

48. The apparatus of claim 41, wherein the sensor derived data comprises a position calculation generated by a smartphone comprising a triangulation positioner.

49. The apparatus of claim 41, wherein the sensor derived data comprises a position generated by a smartphone comprising a barcode imager.

50. The apparatus of claim 41, wherein the sensor derived data comprises a position calculation determined by a triangulation positioner, and wherein the sensor-derived data is determined, at least in part, based on DGPS correction data.

51. A method for assessing a health, fitness, operation, or performance of an individual, the method comprising:

receiving tag derived data comprising tag location data and blink data;

comparing the tag location data to one or more individual dynamics/kinetics models;

and

determining a health, fitness, operation and performance (HFOP) status for the individual based on the comparing the tag location data to one or more individual dynamics/kinetics models.

52. The method according to claim 51, wherein the tag location data is determined based at least in part on the blink data.

53. The method according to claim 51, wherein the tag derived data further comprises a tag-individual correlator.

54. The method according to claim 51, wherein the tag location data comprises tag location data for each of two or more location tags, each indicative of a position for a different part of the individual.

55. The method according to claim 51, further comprising:

determining whether the HFOP status satisfies a threshold value and generating, in an instance in which the HFOP status does satisfy the threshold value, an alert.

56. The method according to claim 51, wherein the one or more individual dynamics/kinetics models with which the tag location data is compared is associated with at least one of role information associated with the individual.

57. The method according to claim 51, wherein the one or more individual dynamics/kinetics models with which the tag location data is compared is associated with at least an individual identity associated the individual.

58. The method according to claim 51, wherein the one or more individual dynamics/kinetics models with which the tag location data is compared is associated with at least zone data associated with the individual.

59. The method according to claim 51, wherein the individual dynamics/kinetics models comprise location history data for the individual.

60. A method for assessing the health, fitness, operation, or performance of an individual, the method comprising:

receiving sensor derived data indicative of at least one of a health, a fitness, an operation level, or a performance level for the individual;

comparing the sensor derived data to a health, fitness, operation and performance (HFOP) model; and

determining a HFOP status for the individual based on the comparing the sensor derived data to at least one of health models, fitness models, operation level models, or performance level models.

61. A method according to claim 60, further comprising:

receiving tag derived data comprising tag location data and blink data; and

comparing the tag location data to individual dynamics/kinetics models;

wherein the determination of the HFOP status for the individual is further based on the comparing the tag location data to one or more individual dynamics/kinetics models.

62. A method according to claim 60, wherein the at least one of health models, fitness models, operation level models, or performance level models comprise a respective one of a health history data, a fitness history data, an operation level history data, or a performance level history data.

63. The method according to claim 60 further comprising:

comparing the sensor derived data to pre-defined thresholds, and generating an alert in an instance in which particular sensor derived data satisfies an associated threshold.

64. The method according to claim 60, wherein the one or more health models, fitness models, operation level models, or performance level models with which the sensor derived data is compared is associated with at least one of role information associated with the individual, an individual identity associated the individual, or zone data associated with the individual.

65. A computer program product for assessing a health, fitness, operation, or performance of an individual, the computer program product comprising at least one computer-readable storage medium having computer-executable program code instructions stored therein, the computer-executable program code instructions comprising program code instructions for:

receiving tag derived data comprising tag location data and blink data;

comparing the tag location data to one or more individual dynamics/kinetics models; and

determining a health, fitness, operation and performance (HFOP) status for the individual based on the comparing the tag location data to one or more individual dynamics/kinetics models.

66. The computer program product according to claim 65, wherein the tag location data is determined based at least in part on the blink data.

67. The computer program product according to claim 65, wherein the tag derived data further comprises a tag-individual correlator.

68. The computer program product according to claim 65, wherein the tag location data comprises tag location data for each of two or more location tags, each indicative of a position for a different part of the individual.

69. The computer program product according to claim 65, wherein the computer-executable program code instructions further comprise program code instructions for:

determining whether the HFOP status satisfies a threshold value; and

generating, in an instance in which the HFOP status does satisfy the threshold value, an alert.

70. The computer program product according to claim 65, wherein the one or more individual dynamics/kinetics models with which the tag location data is compared is associated with at least role information associated with the individual.

71. The computer program product according to claim 65, wherein the one or more individual dynamics/kinetics models with which the tag location data is compared is associated with at least an individual identity associated with the individual.

72. The computer program product according to claim 65, wherein the one or more individual dynamics/kinetics models with which the tag location data is compared is associated with at least zone data associated with the individual.

73. The computer program product according to claim 65, wherein the individual dynamics/kinetics models comprise location history data for the individual.

74. A computer program product for assessing a health, fitness, operation, or performance of an individual, comprising at least one computer-readable storage medium having computer-executable program code instructions stored therein, the computer-executable program code instructions comprising program code instructions for:

receiving sensor derived data indicative of at least one of a health, a fitness, an operation level, or a performance level for the individual;

comparing the sensor derived data to at least one of health models, fitness models, operation level models, and performance level models; and

determining a health, fitness, operation and performance (HFOP) status for the individual based on the comparing the sensor derived data to at least one of health models, fitness models, operation level models, or performance level models.

75. The computer program product according to claim 74, wherein the at least one of health models, fitness models, operation level models, or performance level models comprise a respective one of a health history data, a fitness history data, an operation level history data, or a performance level history data.

76. The computer program product according to claim 74, wherein the computer-executable program code portions further comprise program code instructions for:

comparing the sensor derived data to pre-defined thresholds, and generating an alert in an instance in which particular sensor derived data satisfies an associated threshold.

77. The computer program product according to claim 74, wherein the one or more health models, fitness models, operation level models, or performance level models with which the tag location data is compared is associated with at least one of role information associated with the individual, an individual identity associated the individual, or zone data associated with the individual.

78. An apparatus for assessing a health, fitness, operation, or performance of an individual, the apparatus comprising a processor and a memory having computer code stored therein, the computer code configured, when executed by the processor, to cause the apparatus to:

receive tag derived data comprising tag location data and blink data;

compare the tag location data to one or more individual dynamics/kinetics models; and

determine a health, fitness, operation and performance (HFOP) status for the individual based on the comparison of the tag location data to one or more individual dynamics/kinetics models.

79. The apparatus according to claim 78, wherein the tag location data is determined based at least in part on the blink data.

80. The apparatus according to claim 78, wherein the tag derived data further comprises a tag-individual correlator.

81. The apparatus according to claim 78, wherein the tag location data comprises tag location data for each of two or more location tags, each indicative of a position for a different part of the individual.

82. The apparatus according to claim 78, wherein the computer code is further configured, when executed by the processor, to cause the apparatus to:

determine whether the HFOP status satisfies a threshold value and generating, in an instance in which the HFOP status does satisfy the threshold value, an alert.

83. The apparatus according to claim 78, wherein the one or more individual dynamics/kinetics models with which the tag location data is compared is associated with at least role information associated with the individual.

84. The apparatus according to claim 78, wherein the one or more individual dynamics/kinetics models with which the tag location data is compared is associated with at least an individual identity associated with the individual.

85. The apparatus according to claim 78, wherein the one or more individual dynamics/kinetics models with which the tag location data is compared is associated with at least zone data associated with the individual.

86. The apparatus according to claim 78, wherein the individual dynamics/kinetics models comprise location history data for the individual.

87. An apparatus for assessing the health, fitness, operation, or performance of an individual, the apparatus comprising a processor and a memory having computer code stored therein, the computer code configured, when executed by the processor, to cause the apparatus to:

receive sensor derived data indicative of at least one of a health, a fitness, an operation level, or a performance level for the individual;

compare the sensor derived data to at least one of health models, fitness models, operation level models and performance level models; and

determine a health, fitness, operation and performance (HFOP) status for the individual based on the comparing the sensor derived data to at least one of health models, fitness models, operation level models, or performance level models.

88. The apparatus according to claim 87, wherein the at least one of health models, fitness models, operation level models, or performance level models comprise a respective one of a health history data, a fitness history data, an operation level history data, or a performance level history data.

89. The apparatus according to claim 87, wherein the computer code is further configured, when executed by the processor, to cause the apparatus to:

compare the sensor derived data to pre-defined thresholds, and generate an alert in an instance in which particular sensor derived data satisfies an associated threshold.

90. The apparatus according to claim 87, wherein the one or more health models, fitness models, operation level models, or performance level models with which the sensor derived data is compared is associated with at least one of role information associated with the individual, an individual identity associated the individual, or zone data associated with the individual.

91. A method for monitoring an individual associated with a radio frequency (RF) location tag, the method comprising:

receiving tag derived data comprising tag location data and blink data, wherein the tag location data is determined based at least in part on the blink data;

receiving sensor derived data indicative of at least one of a health, a fitness, an operation level, or a performance level for the individual;

receiving a tag-individual correlator associated with the individual;

receiving a tag-sensor correlator associated with the RF location tag; and

matching the tag-sensor correlator with the tag-individual correlator to associate the sensor-derived data with the individual.

92. The method according to claim 91, wherein the tag-individual correlator is received from an individual database.

93. The method according to claim 91, wherein the tag-individual correlator is determined from the blink data.

94. The method according to claim 91, wherein the tag-sensor correlator is determined from the blink data.

95. A method for associating an environmental measurement with an individual, the method comprising:

associating an individual location with the individual;

receiving, from a sensor, a sensor signal comprising one or more environmental measurements indicative of at least one of a health, a fitness, an operation level, or a performance level for the individual;

associating a sensor location with the sensor; and

determining a sensor-individual correlator based on the individual location being within a threshold distance of the sensor location.

96. The method according to claim 95, wherein associating the individual location with the individual comprises:

receiving a tag signal comprising blink data from a radio frequency (RF) location tag associated with the individual; and

determining the individual location associated with the individual based at least in part on the blink data.

97. The method according to claim 95, wherein associating the individual location with the individual comprises:

receiving, from a second sensor associated with the individual, a second sensor signal comprising a position calculation, the position calculation determined by a triangulation positioner; and

determining the individual location associated with the individual based at least in part on the position calculation.

98. The method according to claim 95, wherein associating the individual location with the individual comprises:

receiving, from a second sensor associated with the individual, a second sensor signal comprising position information, the position information accessed from a proximity label; and

determining the individual location associated with the individual based at least in part on the position information.

99. The method according to claim 95, wherein associating the sensor location with the sensor comprises:

receiving a tag signal comprising blink data from a radio frequency (RF) location tag associated with the sensor; and

determining the sensor location associated with the sensor based at least in part on the blink data.

100. The method according to claim 95, wherein associating the sensor location with the sensor comprises:

receiving a tag signal comprising blink data from a radio frequency (RF) reference tag associated with the sensor, wherein said blink data includes a tag unique identification number;

determining a location of the RF reference tag from a Geographical Information System based at least, in part, on the tag unique identification number; and

determining the sensor location associated with the sensor based on the location of the RF reference tag.

101. The method according to claim 95, wherein the sensor signal received from the sensor further comprises a position calculation determined by a triangulation positioner, wherein the sensor location associated with the sensor is determined at least in part on the position calculation.

102. The method according to claim 95, wherein the sensor signal received from the sensor further comprises position information read from a proximity label, wherein the sensor location associated with the sensor is determined at least in part on the position information.

103. The method according to claim 97, further comprising:

receiving a differential global positioning system (DGPS) correction signal; and

determining the individual location associated with the individual based on the DGPS correction signal.

104. The method according to claim 101 further comprising:

receiving a differential global positioning system (DGPS) correction signal; and

determining the sensor location based on the DGPS correction signal.

105. The method according to claim 102 further comprising:

receiving a differential global positioning system (DGPS) correction signal; and

determining the sensor location based on the DGPS correction signal.

106. A method for associating an environmental measurement with an individual, the method comprising:

associating an individual location with the individual;

receiving, from a sensor, a sensor signal comprising one or more environmental measurements indicative of at least one of a health, a fitness, an operation level, or a performance level for the individual;

associating a sensor location with the sensor; and

associating the sensor with the individual based on the individual location being within a threshold distance of the sensor location.

107. The method according to claim 106, further comprising:

associating a sensor information packet, derived from the sensor signal, with the individual.

108. A computer program product for monitoring an individual associated with a radio frequency (RF) location tag, comprising at least one computer-readable storage medium having computer-executable program code instructions stored therein, the computer-executable program code instructions comprising program code instructions for:

receiving tag derived data comprising tag location data and blink data,

wherein the tag location data is determined based at least in part on the blink data;

receiving sensor derived data indicative of at least one of a health, a fitness, an operation level, or a performance level for the individual;

receiving a tag-individual correlator associated with the individual;

receiving a tag-sensor correlator associated with the RF location tag; and

matching the tag-sensor correlator with the tag-individual correlator to associate the sensor-derived data with the individual.

109. The computer program product according to claim 108, wherein the tag-individual correlator is received from an individual database.

110. The computer program product according to claim 108, wherein the tag-individual correlator is determined from the blink data.

111. The computer program product according to claim 108, wherein the tag-sensor correlator is determined from the blink data.

112. A computer program product for associating an environmental measurement with an individual comprising at least one computer-readable storage medium having computer-executable program code instructions stored therein, the computer-executable program code instructions comprising program code instructions for:

associating an individual location with the individual;

receiving, from a sensor, a sensor signal comprising one or more environmental measurements indicative of at least one of a health, a fitness, an operation level, or a performance level for the individual;

associating a sensor location with the sensor; and

determining a sensor-individual correlator based on the individual location being within a threshold distance of the sensor location.

113. The computer program product according to claim 112, wherein associating the individual location with the individual comprises:

receiving a tag signal comprising blink data from a radio frequency (RF) location tag associated with the individual; and

determining the individual location associated with the individual based at least in part on the blink data.

114. The computer program product according to claim 112, wherein associating the individual location with the individual comprises:

receiving, from a second sensor associated with the individual, a second sensor signal comprising a position calculation, the position calculation determined by a triangulation positioner; and

determining the individual location associated with the individual based at least in part on the position calculation.

115. The computer program product according to claim 112, wherein associating the individual location with the individual comprises:

receiving, from a second sensor associated with the individual, a second sensor signal comprising position information, the position information accessed from a proximity label; and

determining the individual location associated with the individual based at least in part on the position information.

116. The computer program product according to claim 112, wherein associating the sensor location with the sensor comprises:

receiving a tag signal comprising blink data from a radio frequency (RF) location tag associated with the sensor; and

determining the sensor location associated with the sensor based at least in part on the blink data.

117. The computer program product according to claim 112, wherein associating the sensor location with the sensor comprises:

receiving a tag signal comprising blink data from a radio frequency (RF) reference tag associated with the sensor, wherein said blink data includes a tag unique identification number;

determining a location of the RF reference tag from a Geographical Information System based at least, in part, on the tag unique identification number; and

determining the sensor location associated with the sensor based on the location of the RF reference tag.

118. The computer program product according to claim 112, wherein the sensor signal received from the sensor further comprises a position calculation determined by a triangulation positioner, wherein the sensor location associated with the sensor is determined at least in part on the position calculation.

119. The computer program product according to claim 112, wherein the sensor signal received from the sensor further comprises position information read from a proximity label, wherein the sensor location associated with the sensor is determined at least in part on the position information.

120. The computer program product according to claim 118 wherein the computer-executable program code instructions further comprise program code instructions for:

receiving a differential global positioning system (DGPS) correction signal; and

determining the individual location associated with the individual based on the DGPS correction signal.

121. The computer program product according to claim 118, wherein the computer-executable program code instructions further comprise program code instructions for:

receiving a differential global positioning system (DGPS) correction signal; and

determining the sensor location based on the DGPS correction signal.

122. The computer program product according to claim 119, wherein the computer-executable program code instructions further comprise program code instructions for:

receiving a differential global positioning system (DGPS) correction signal; and

determining the sensor location based on the DGPS correction signal.

123. An apparatus for monitoring an individual associated with a radio frequency (RF) location tag, the apparatus comprising a processor and a memory having computer code stored therein, the computer code configured, when executed by the processor, to cause the apparatus to:

receive tag derived data comprising tag location data and blink data,

wherein the tag location data is determined based at least in part on the blink data;

receive sensor derived data indicative of at least one of a health, a fitness, an operation level, or a performance level for the individual;

receive a tag-individual correlator associated with the individual;

receive a tag-sensor correlator associated with the RF location tag; and

match the tag-sensor correlator with the tag-individual correlator to associate the sensor-derived data with the individual.

124. The apparatus according to claim 123, wherein the tag-individual correlator is received from an individual database.

125. The apparatus according to claim 123, wherein the tag-individual correlator is determined from the blink data.

126. The apparatus according to claim 123, wherein the tag-sensor correlator is determined from the blink data.

127. The apparatus according to claim 123, wherein the tag-sensor correlator is determined from the sensor-derived data.

128. An apparatus for associating an environmental measurement with an individual, the apparatus comprising a processor and a memory having computer code stored therein, the computer code configured, when executed by the processor, to cause the apparatus to:

associate an individual location with the individual;

receive, from a sensor, a sensor signal comprising one or more environmental measurements indicative of at least one of a health, a fitness, an operation level, or a performance level for the individual;

associate a sensor location with the sensor; and

determine a sensor-individual correlator based on the individual location being within a threshold distance of the sensor location.

129. The apparatus according to claim 128, wherein associating the individual location with the individual comprises:

receiving a tag signal comprising blink data from a radio frequency (RF) location tag associated with the individual; and

determining the individual location associated with the individual based at least in part on the blink data.

130. The apparatus according to claim 128, wherein associating the individual location with the individual comprises:

receiving, from a second sensor associated with the individual, a second sensor signal comprising a position calculation, the position calculation determined by a triangulation positioner; and

determining the individual location associated with the individual based at least in part on the position calculation.

131. The apparatus according to claim 128, wherein associating the individual location with the individual comprises:

receiving, from a second sensor associated with the individual, a second sensor signal comprising position information, the position information accessed from a proximity label; and

determining the individual location associated with the individual based at least in part on the position information.

132. The apparatus according to claim 128, wherein associating the sensor location with the sensor comprises:

receiving a tag signal comprising blink data from a radio frequency (RF) location tag associated with the sensor; and

determining the sensor location associated with the sensor based at least in part on the blink data.

133. The apparatus according to claim 128, wherein associating the sensor location with the sensor comprises:

receiving a tag signal comprising blink data from a radio frequency (RF) reference tag associated with the sensor, wherein said blink data includes a tag unique identification number;

determining a location of the RF reference tag from a Geographical Information System based at least, in part, on the tag unique identification number; and

determining the sensor location associated with the sensor based on the location of the RF reference tag.

134. The apparatus according to claim 128, wherein the sensor signal received from the sensor further comprises a position calculation determined by a triangulation positioner, wherein the sensor location associated with the sensor is determined at least in part on the position calculation.

135. The apparatus according to claim 128, wherein the sensor signal received from the sensor further comprises position information read from a proximity label, wherein the sensor location associated with the sensor is determined at least in part on the position information.

136. The apparatus according to claim 134, wherein the computer code is further configured, when executed by the processor, to cause the apparatus to:

receive a differential global positioning system (DGPS) correction signal; and

determine the sensor location based on the DGPS correction signal.

137. The apparatus according to claim 135, wherein the computer code is further configured, when executed by the processor, to cause the apparatus to:

receive a differential global positioning system (DGPS) correction signal; and

determine the sensor location based on the DGPS correction signal.

138. An apparatus for associating an environmental measurement with an individual, the apparatus comprising a processor and a memory having computer code stored therein, the computer code configured, when executed by the processor, to cause the apparatus to:

associate an individual location with the individual;

receive, from a sensor, a sensor signal comprising one or more environmental measurements indicative of at least one of a health, a fitness, an operation level, or a performance level for the individual;

associate a sensor location with the sensor; and

associate the sensor with an individual based on the individual location being within a threshold distance of the sensor location.

139. An apparatus for associating an environmental measurement with an individual, the apparatus comprising a processor and a memory having computer code stored therein, the computer code configured, when executed by the processor, to cause the apparatus to:

associate an individual location with the individual;

receive, from a sensor, a sensor signal comprising a sensor information packet;

associate a sensor location with the sensor; and

associate the sensor information packet with the individual based on the individual location being within a threshold distance of the sensor location.

140. A method for assessing a health or fitness of an individual, the method comprising:

receiving tag derived data comprising tag location data and blink data;

selecting an individual dynamics/kinetics model from an individual dynamics/kinetics models database;

comparing the tag location data to the individual dynamics/kinetics model; and

determining a health, fitness, operation and performance (HFOP) status for the individual based on the comparison of the tag location data to the individual dynamics/kinetics model.

141. The method according to claim 140, wherein selecting the individual dynamics/kinetics model is based on at least an individual identity.

142. The method according to claim 140, wherein selecting the individual dynamics/kinetics model is based on at least a zone determined from the tag location data.

143. The method according to claim 140, wherein selecting the individual dynamics/kinetics model is based on at least a role.

144. The method according to claim 140, wherein selecting the individual dynamics/kinetics model is based on at least a role and an individual identity.

145. The method according to claim 140, wherein comparing the tag location data to the individual dynamics/kinetics model is based at least partially on adversarial data.

146. The method according to claim 145,

wherein the tag derived data comprises first blink data received from a first location tag associated with a first individual and second blink data received from a second location tag associated with a second individual, and

wherein the adversarial data is based at least partially on comparing the first blink data to the second blink data.

147. The method according to claim 140, wherein comparing the tag location data to the individual dynamics/kinetics model comprises matching one or more field values of the tag location data to one or more corresponding field values of the individual dynamics/kinetic model.

148. The method according to claim 140, wherein comparing the tag location data to the individual dynamics/kinetics model comprises determining that at least one field value of the tag location data has exceeded a control limit defined by the individual dynamics/kinetic model.

149. The method according to claim 140, wherein comparing the tag location data to the individual dynamics/kinetics model comprises determining that at least one field value of the tag location data is trending.

150. The method according to claim 140, wherein comparing the tag location data to the individual dynamics/kinetics model comprises determining that the tag location data falls within a cluster range.

151. The method according to claim 140, wherein comparing the tag location data to the individual dynamics/kinetics model comprises calculating the covariance of the tag location data with the individual dynamics/kinetics model.

152. A method for assessing a health, fitness, operation, or performance of an individual in a monitored area, the method comprising:

receiving tag derived data comprising tag location data and blink data, wherein the tag location data is determined based at least in part on the blink data;

receiving sensor derived data indicative of at least one of a health, a fitness, an operation level, or a performance level for the individual;

selecting a health, fitness, operation and performance (HFOP) model based on at least a zone associated with or determined from the tag location data;

comparing the sensor derived data to the HFOP model; and

determining a HFOP status for the individual based on the comparison of the sensor derived data to at least one of health models, fitness models, operation level models, or performance level models.

153. The method according to claim 152, wherein selecting a HFOP model is based on at least an individual identity.

154. The method according to claim 152, wherein selecting a HFOP model is based on at least a role.

155. The method according to claim 152, wherein selecting a HFOP model is based on at least a role and an individual identity.

156. A method for monitoring an individual, the method comprising:

receiving tag derived data for each of two or more tags, the tag derived data comprising tag location data and blink data, wherein the tag location data is determined based at least in part on the blink data;

determining, based on an individual role database, that the two or more tags are associated with individuals in an adversarial role; and

determining adversarial data based on the tag location data.

157. The method according to claim 156, further comprising:

selecting an individual dynamics/kinetics model from an individual dynamics/kinetics models database based on the adversarial data;

comparing the tag location data of at least one of the tags to the individual dynamics/kinetics model;

and

determining a health, fitness, operation and performance (HFOP) status for the individual associated with the at least one tag based on the comparison of the tag location data to the individual dynamics/kinetics model.

158. The method according to claim 156, further comprising:

receiving sensor derived data indicative of at least one of a health, a fitness, an operation level, or a performance level for at least one individual associated with at least one of the tags;

selecting a health, fitness, operation and performance (HFOP) model based on at least the adversarial data;

comparing the sensor derived data to the HFOP model; and

determining a HFOP status for the individual based on the comparison of the sensor derived data to at least one of health models, fitness models, operation level models, or performance level models.

159. The method according to claim 156, further comprising:

selecting an individual dynamics/kinetics model from an individual dynamics/kinetics models database based on the adversarial data;

comparing the tag location data of at least one of the tags to the individual dynamics/kinetics model;

receiving sensor derived data indicative of at least one of a health, a fitness, an operation level, or a performance level for at least one individual associated with at least one of the tags;

selecting a health, fitness, operation and performance (HFOP) model based on at least the adversarial data;

comparing the sensor derived data to the HFOP model; and

determining a HFOP status for the individual based on the comparison of the sensor derived data to at least one of health models, fitness models, operation level models, or performance level models and based on the comparison of the tag location data to the individual dynamics/kinetics model.

160. A computer program product comprising at least one computer-readable storage medium having computer-executable program code instructions stored therein, the computer-executable program code instructions comprising program code instructions for:

receiving tag derived data comprising tag location data and blink data;

selecting an individual dynamics/kinetics model from an individual dynamics/kinetics models database;

comparing the tag location data to the individual dynamics/kinetics model; and

determining a health, fitness, operation and performance (HFOP) status for the individual based on the comparison of the tag location data to the individual dynamics/kinetics model.

161. The computer program product according to claim 160, wherein selecting the individual dynamics/kinetics model is based on at least an individual identity.

162. The computer program product according to claim 160, wherein selecting the individual dynamics/kinetics model is based on at least a zone determined from the tag location data.

163. The computer program product according to claim 160, wherein selecting the individual dynamics/kinetics model is based on at least a role.

164. The computer program product according to claim 160, wherein selecting the individual dynamics/kinetics model is based on at least a role and an individual identity.

165. The computer program product according to claim 160, wherein comparing the tag location data to the individual dynamics/kinetics model is based at least partially on adversarial data.

166. The computer program product according to claim 165,

wherein the tag derived data comprises first blink data received from a first location tag associated with a first individual and second blink data received from a second location tag associated with a second individual, and

wherein the adversarial data is based at least partially on comparing the first blink data to the second blink data.

167. The computer program product according to claim 160, wherein comparing the tag location data to the individual dynamics/kinetics model comprises matching one or more field values of the tag location data to one or more corresponding field values of the individual dynamics/kinetic model.

168. The computer program product according to claim 160, wherein comparing the tag location data to the individual dynamics/kinetics model comprises determining that at least one field value of the tag location data has exceeded a control limit defined by the individual dynamics/kinetic model.

169. The computer program product according to claim 160, wherein comparing the tag location data to the individual dynamics/kinetics model comprises determining that at least one field value of the tag location data is trending.

170. The computer program product according to claim 160, wherein comparing the tag location data to the individual dynamics/kinetics model comprises determining that the tag location data falls within a cluster range.

171. The computer program product according to claim 160, wherein comparing the tag location data to the individual dynamics/kinetics model comprises calculating the covariance of the tag location data with the individual dynamics/kinetics model.

172. A computer program product for assessing a health, fitness, operation, or performance of an individual in a monitored area, the computer program product comprising at least one computer-readable storage medium having computer-executable program code instructions stored therein, the computer-executable program code instructions comprising program code instructions for:

receiving tag derived data comprising tag location data and blink data, wherein the tag location data is determined based at least in part on the blink data;

receiving sensor derived data indicative of at least one of a health, a fitness, an operation level, or a performance level for the individual;

selecting a health, fitness, operation and performance (HFOP) model based on at least a zone associated with or determined from the tag location data;

comparing the sensor derived data to the HFOP model; and

determining a HFOP status for the individual based on the comparison of the sensor derived data to at least one of health models, fitness models, operation level models, or performance level models.

173. The computer program product according to claim 172, wherein selecting a HFOP model is based on at least an individual identity.

174. The computer program product according to claim 172, wherein selecting a HFOP model is based on at least a role.

175. The computer program product according to claim 172, wherein selecting a HFOP model is based on at least a role and an individual identity.

176. A computer program product for monitoring an individual, the computer program product comprising at least one computer-readable storage medium having computer-executable program code instructions stored therein, the computer-executable program code instructions comprising program code instructions for:

receiving tag derived data for each of two or more tags, the tag derived data comprising tag location data and blink data, wherein the tag location data is determined based at least in part on the blink data;

determining, based on an individual role database, that the two or more tags are associated with individuals in an adversarial role;

determining adversarial data based on the tag location data.

177. The computer program product according to claim 176, wherein the computer-executable program code instructions further comprise program code instructions for:

selecting an individual dynamics/kinetics model from an individual dynamics/kinetics models database based on the adversarial data;

comparing the tag location data of at least one of the tags to the individual dynamics/kinetics model;

and

determining a health, fitness, operation and performance (HFOP) status for the individual associated with the at least one tag based on the comparison of the tag location data to the individual dynamics/kinetics model.

178. The computer program product according to claim 176, wherein the computer-executable program code instructions further comprise program code instructions for:

receiving sensor derived data indicative of at least one of a health, a fitness, an operation level, or a performance level for at least one individual associated with at least one of the tags;

selecting a health, fitness, operation and performance (HFOP) model based on at least the adversarial data;

comparing the sensor derived data to the HFOP model; and

determining a HFOP status for the individual based on the comparison of the sensor derived data to at least one of health models, fitness models, operation level models, or performance level models.

179. The computer program product according to claim 176, wherein the computer-executable program code instructions further comprise program code instructions for:

selecting an individual dynamics/kinetics model from an individual dynamics/kinetics models database based on the adversarial data;

comparing the tag location data of at least one of the tags to the individual dynamics/kinetics model;

receiving sensor derived data indicative of at least one of a health, a fitness, an operation level, or a performance level for at least one individual associated with at least one of the tags;

selecting a health, fitness, operation and performance (HFOP) model based on at least the adversarial data;

comparing the sensor derived data to the HFOP model; and

determining a HFOP status for the individual based on the comparison of the sensor derived data to at least one of health models, fitness models, operation level models, or performance level models and based on the comparison of the tag location data to the individual dynamics/kinetics model.

180. An apparatus for assessing a health or fitness of an individual, the apparatus comprising a processor and a memory having computer code stored therein, the computer code configured, when executed by the processor, to cause the apparatus to:

receive tag derived data comprising tag location data and blink data;

select an individual dynamics/kinetics model from an individual dynamics/kinetics models database;

compare the tag location data to the individual dynamics/kinetics model;

and

determine a health, fitness, operation and performance (HFOP) status for the individual based on the comparison of the tag location data to the individual dynamics/kinetics model.

181. The apparatus according to claim 180, wherein selecting the individual dynamics/kinetics model is based on at least an individual identity.

182. The apparatus according to claim 180, wherein selecting the individual dynamics/kinetics model is based on at least a zone determined from the tag location data.

183. The apparatus according to claim 180, wherein selecting the individual dynamics/kinetics model is based on at least a role.

184. The apparatus according to claim 180, wherein selecting the individual dynamics/kinetics model is based on at least a role and an individual identity.

185. The apparatus according to claim 180, wherein comparing the tag location data to the individual dynamics/kinetics model is based at least partially on adversarial data.

186. The apparatus according to claim 185,

wherein the tag derived data comprises first blink data received from a first location tag associated with a first individual and second blink data received from a second location tag associated with a second individual, and

wherein the adversarial data is based at least partially on comparing the first blink data to the second blink data.

187. The apparatus according to claim 180, wherein comparing the tag location data to the individual dynamics/kinetics model comprises matching one or more field values of the tag location data to one or more corresponding field values of the individual dynamics/kinetic model.

188. The apparatus according to claim 180, wherein comparing the tag location data to the individual dynamics/kinetics model comprises determining that at least one field value of the tag location data has exceeded a control limit defined by the individual dynamics/kinetic model.

189. The apparatus according to claim 180, wherein comparing the tag location data to the individual dynamics/kinetics model comprises determining that at least one field value of the tag location data is trending.

190. The apparatus according to claim 180, wherein comparing the tag location data to the individual dynamics/kinetics model comprises determining that the tag location data falls within a cluster range.

191. The apparatus according to claim 180, wherein comparing the tag location data to the individual dynamics/kinetics model comprises calculating the covariance of the tag location data with the individual dynamics/kinetics model.

192. An apparatus for assessing a health, fitness, operation, or performance of an individual in a monitored area, the apparatus comprising a processor and a memory having computer code stored therein, the computer code configured, when executed by the processor, to cause the apparatus to:

receive tag derived data comprising tag location data and blink data, wherein the tag location data is determined based at least in part on the blink data;

receive sensor derived data indicative of at least one of a health, a fitness, an operation level, or a performance level for the individual;

select a health, fitness, operation and performance (HFOP) model based on at least a zone associated with or determined from the tag location data;

compare the sensor derived data to the HFOP model; and

determine a HFOP status for the individual based on the comparison of the sensor derived data to at least one of health models, fitness models, operation level models, or performance level models.

193. The apparatus according to claim 192, wherein selecting a HFOP model is based on at least an individual identity.

194. The apparatus according to claim 192, wherein selecting a HFOP model is based on at least a role.

195. The apparatus according to claim 192, wherein selecting a HFOP model is based on at least a role and an individual identity.

196. An apparatus for monitoring an individual, the apparatus comprising a processor and a memory having computer code stored therein, the computer code configured, when executed by the processor, to cause the apparatus to:

receive tag derived data for each of two or more tags, the tag derived data comprising tag location data and blink data, wherein the tag location data is determined based at least in part on the blink data;

determine, based on an individual role database, that the two or more tags are associated with individuals in an adversarial role; and

determine adversarial data based on the tag location data.

197. The apparatus according to claim 196, wherein the computer code is further configured, when executed by the processor, to cause the apparatus to:

select an individual dynamics/kinetics model from an individual dynamics/kinetics models database based on the adversarial data;

compare the tag location data of at least one of the tags to the individual dynamics/kinetics model;

and

determine a health, fitness, operation and performance (HFOP) status for the individual associated with the at least one tag based on the comparison of the tag location data to the individual dynamics/kinetics model.

198. The apparatus according to claim 196, wherein the computer code is further configured, when executed by the processor, to cause the apparatus to:

receive sensor derived data indicative of at least one of a health, a fitness, an operation level, or a performance level for at least one individual associated with at least one of the tags;

select a health, fitness, operation and performance (HFOP) model based on at least the adversarial data;

compare the sensor derived data to the HFOP model; and

determine a HFOP status for the individual based on the comparison of the sensor derived data to at least one of health models, fitness models, operation level models, or performance level models.

199. The apparatus according to claim 196, wherein the computer code is further configured, when executed by the processor, to cause the apparatus to:

select an individual dynamics/kinetics model from an individual dynamics/kinetics models database based on the adversarial data;

compare the tag location data of at least one of the tags to the individual dynamics/kinetics model;

receive sensor derived data indicative of at least one of a health, a fitness, an operation level, or a performance level for at least one individual associated with at least one of the tags;

select a health, fitness, operation and performance (HFOP) model based on at least the adversarial data;

compare the sensor derived data to the HFOP model; and

determine a HFOP status for the individual based on the comparison of the sensor derived data to at least one of health models, fitness models, operation level models, or performance level models and based on the comparison of the tag location data to the individual dynamics/kinetics model.

200. A system for monitoring an individual, the system comprising:

one or more tags; and

apparatus comprising a processor and a memory having computer code stored therein, the computer code configured, when executed by the processor, to cause the apparatus to;

receive tag derived data comprising tag location data and blink data, wherein the tag location data is determined based at least in part on the blink data;

receive sensor derived data indicative of at least one of a health, a fitness, an operation level, or a performance level for an individual;

compare the tag location data to individual dynamics/kinetics models;

compare the sensor derived data to at least one of health models, fitness models, operation level models, or performance level models; and

determine a health, fitness, operation and performance (HFOP) status for the individual based on the comparing the tag location data to individual dynamics/kinetics models and on the comparing the sensor derived data to at least one of health models, fitness models, operation level models, or performance level models.

201. The system according to claim 200, further comprising:

one or more sensors.