System and method for coach decision support

• US 9,501,950 B2
• Filed: 11/07/2014
• Issued: 11/22/2016
• Est. Priority Date: 11/07/2014
• Status: Expired due to Fees

• Current Assignee: Umm Al-Qura University
• Original Assignee: Umm Al-Qura University
• Inventors: Kattan, Ahmed, Arif, Muhammad
• Primary Examiner(s): DEODHAR, OMKAR A
• Application Number:
  US14/400,862
• Publication Number:
  US 20160133152A1
• Time in Patent Office: 746 Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2503/10 Athletes
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A63B 24/0062 Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
• CPC Class Codes: G01S 19/05 providing aiding data
• CPC Class Codes: G01S 19/19 Sporting applications
• CPC Class Codes: G06Q 99/00 Subject matter not provided for in other groups of this subclass
• CPC Class Codes: G09B 19/0038 Sports
• CPC Class Codes: G09B 5/00 Electrically-operated educational appliances working with questions and answers G09B7/00; simulators G09B9/00; advertising or displaying in general G09F
• CPC Class Codes: G16H 20/70 relating to mental therapies, e.g. psychological therapy or autogenous training

Abstract

A coach decision support method including collecting sensor data associated with an athlete at a data logger circuitry; wherein sensor data is generated by at least one sensor placed on the athlete; pre-processing the sensor data at the data logger circuitry and filtering sensor noise from the collected sensor data; extracting at least one feature of the collected sensor data; classifying the at least one extracted feature into at least one physical activity; transmitting the physical activity classification to a data analysis module; generating a detailed report of the physical activity analyzing the athlete'"'"'s performance within a sporting arena; and generating at least one coach decision recommendation based at least in part on the generated detailed report.

Claims

1. A coach decision support method comprising:

collecting sensor data associated with an athlete at a data logger circuitry, wherein the sensor data is generated by at least one sensor placed on the athlete;

pre-processing the sensor data at the data logger circuitry by filtering sensor noise from the sensor data;

extracting at least one feature set from the collected sensor data;

classifying with the circuitry the at least one extracted feature set into at least one physical activity;

transmitting the physical activity classification to a data analysis circuitry;

generating with the data analysis circuitry a report of the physical activity indicative of a performance of the athlete within a sporting venue;

generating with the data analysis circuitry at least one coach decision recommendation based at least in part on the report;

generating a coach decision support algorithm to provide intelligent monitoring of the athlete and at least one other athlete within the sporting venue; and

outputting at least one optimal decision recommendation as a function of generated individual movement patterns, individual energy expenditure patterns, and team movement patterns.

2. The method of claim 1, further comprising:

collecting global positioning (GPS) data associated with a position of the athlete;

generating a player localization map; and

transmitting the player localization map to the data analysis module.

3. The method of claim 1, wherein the extracting further includes:

extracting a heart rate associated with the athlete; and

generating an energy expenditure associated with the athlete.

4. The method of claim 3, wherein generating the energy expenditure further includes:

collecting temperature and humidity data;

identifying the physical activity for a predetermined time window; and

calculating with the data analysis circuitry the energy expenditure by the physical activity as a function of the physical activity and the predetermined time window.

5. The method of claim 4, further comprising:

calculating with the data analysis circuitry a heat index as a function of temperature and humidity;

modifying the energy expenditure by the heat index; and

calculating with the data analysis circuitry a total modified energy expenditure of the athlete.

6. The method of claim 1, wherein pre-processing sensor data further includes:

filtering noise associated with at least one sensor to minimize interference in transmitted data between the at least one sensor and at least one other sensor.

7. The method of claim 1, wherein the extracting at least one feature set further includes:

determining at least one body part motion associated with the transmitted sensor data.

8. The method of claim 7, wherein the classifying further includes:

categorizing a combination of the determined at least one body part motion into a classification of movements including any one of standing, walking running, jogging, kicking, dribbling, jumping, or sprinting.

9. The method of claim 1, wherein the detailed report includes:

individual movement patterns of the athlete in relation to at least one moving object and in further relation to at least one other athlete within the sporting venue.

10. The method of claim 9, wherein individual movement patterns further includes:

location and time history of the movement patterns according to the athlete'"'"'s position within the sporting venue; and

moving object possession patterns.

11. The method of claim 9, further comprising:

generating team movement patterns to analyze team activities within the sporting venue, wherein the team movement patterns include team coordinated patterns, moving object passing patterns, and attacking and defending patterns.

12. The method of claim 1, wherein the optimal decision recommendation further includes:

presenting an automated playbook on a user interface, wherein the automated playbook includes at least one play; and

present a generated play recommendation option on the user interface.

13. The method of claim 12, wherein the generated play recommendation is generated based on:

the generated detailed report and further based on a pre-installed playbook; and

a score metric associated with highest probability of success of the play based on the detailed report statistics.

14. The method of claim 1, wherein the optimal decision recommendation further includes:

presenting a player substitution metric on a user interface, wherein the athlete substitution metric is based at least in part on the athlete data in the detailed report.

15. The method of claim 1, wherein the optimal decision recommendation further includes:

generating coaching advice and player motivational tactics on a user interface based on the generated detailed report.

16. The method of claim 1, wherein the optimal decision recommendation further includes:

generating a game analysis report based on the generated detailed report, wherein the game analysis report further includes performance data associated with at least one athlete, a group of athletes, or an entire team; and

generating a training recommendation report based at least in part on the generated detailed report.

17. A coach decision support system comprising:

at least one data generating sensor configured to generate data associated with at least one athlete and further configured to be placed on the at least one athlete; and

electronic circuitry configured to

collect sensor data from the at least one data generating sensor,

pre-process the sensor data to filter sensor noise from the data,

extract at least one feature of the collected sensor data,

classify the at least one extracted feature into at least one physical activity,

transmit the physical activity classification to a data analysis circuitry,

generate a report of the physical activity classification indicative of a performance of the athlete within a sporting venue,

generate at least one coach decision recommendation based at least in part on the report,

generate a coach decision support algorithm to provide intelligent monitoring of the at least one athlete and at least one other athlete within the sporting venue, and

output at least one optimal decision recommendation as a function of generated individual movement patterns, individual energy expenditure patterns, and team movement patterns.

18. The coach decision support system of claim 17, further comprising:

a user-interface module configured to present at least one of an automated playbook and play recommendation, a player substitution recommendation, a coaching advice recommendation, and a game analysis and training recommendation.

19. A non-transitory computer readable storage medium having computer readable instructions that when executed by a processing circuit implement a method of a coach decision support, the method comprising:

collecting sensor data associated with an athlete at a data logger circuitry, wherein the sensor data is generated by at least one sensor placed on the athlete;

pre-processing the sensor data at the data logger circuitry by filtering sensor noise from the sensor data;

extracting at least one feature set from the collected sensor data;

classifying, with processing circuitry, the at least one extracted feature set into at least one physical activity;

transmitting the physical activity classification to a data analysis circuitry;

generating, with the data analysis circuitry, a report of the physical activity indicative of a performance of the athlete within a sporting venue;

generating, with the data analysis circuitry, at least one coach decision recommendation based at least in part on the report;

generating, with the data analysis circuitry, a coach decision support algorithm to provide intelligent monitoring of the athlete and at least one other athlete within the sporting venue; and

outputting, with the data analysis circuitry, at least one optimal decision recommendation as a function of generated individual movement patterns, individual energy expenditure patterns, and team movement patterns.