Adaptive performance trainer

• US 9,173,582 B2
• Filed: 04/23/2010
• Issued: 11/03/2015
• Est. Priority Date: 04/24/2009
• Status: Active Grant

• Current Assignee: Optios Incorporated
• Original Assignee: Advanced Brain Monitoring
• Inventors: Popovic, Djordje, Davis, Gene, Berka, Chris, Behneman, Adrienne, Raphael, Giby
• Primary Examiner(s): Thomson, William
• Assistant Examiner(s): JIAN, SHIRLEY XUEYING
• Application Number:
  US12/766,322
• Publication Number:
  US 20110105859A1
• Time in Patent Office: 2,020 Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2560/0214 of power generation or supply
• CPC Class Codes: A61B 2560/0475 Special features of memory means, e.g. removable memory cards
• CPC Class Codes: A61B 5/0006 ECG or EEG signals
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• 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 5/16 Devices for psychotechnics using teaching or educational appliances G09B1/00 - G09B7/00; Testing reaction times ; Devices for evaluating the psychological state
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/6801 specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7455 characterised by tactile indication, e.g. vibration or electrical stimulation

Abstract

Techniques for accelerating training through optimization of the psychophysiological state of the trainee are provided. These techniques include an adaptive performance training system configured to acquire, analyzed, display, and translate data that reflects the psychophysiological state of the user, including the electrical activity of the brain (EEG), the heart (EKG), the musculature (EMG), respiration and other parameters that characterize the state of the user in real-time. The system includes a plurality of feedback mechanisms for providing visual, auditory, and/or tactile feedback based on the current psychophysiological state of the user and for facilitating moving the user toward a goal psychophysiological state for performing a particular task and for optimizing performance of that task.

Claims

1. A system for accelerating learning of a task using real-time data acquisition and analysis of real-time psychophysiological signals obtained from a user, the system comprising:

one or more hardware processors;

a monitoring module configured to, when executed by the one or more hardware processors, continuously monitor psychophysiological signal data collected from a plurality of sensors attached to a user during a pre-shot period of the task to be learned;

an expertise profiler module configured to

analyze the psychophysiological signal data to generate a current psychophysiological state of the user by computing one or more power spectral density indices from electroencephalographic activity of the user and converting the one or more power spectral density indices into one or more probabilities that the user'"'"'s level of mental effort and engagement belongs to one of a plurality of classes using a discriminant function,

compare the current psychophysiological state of the user to one or more goal states associated with expert performance of the task to be learned, and

generate a set of control signals based on difference between the current psychophysiological state of the user to one or more goal states; and

a real-time feedback generator configured to, when executed by the one or more hardware processors, during the pre-shot period,

receive the control signals from the expertise profiler module, and

generate at least one multimodality feedback signal to provide feedback to guide the user toward the one or more goal states, wherein the multimodality feedback signal is based on the control signals received from the expertise profiler module, wherein the feedback comprises a tactile indication to the user of the user'"'"'s current psychophysiological state at a pace that replicates the user'"'"'s heartbeat, and wherein the tactile indication of the user'"'"'s current psychophysiological state decreases as the user'"'"'s current psychophysiological state approaches an ideal shooting state for the task to be learned.

2. The system of claim 1 wherein the monitoring module is configured to continue to monitor the psychophysiological signal data after the feedback has been delivered to the user;

and wherein the system is configured to determine whether the feedback delivered to the user has helped the user to reach the one or more goal states.

3. The system of claim 1 wherein the monitoring module is further configured to monitor additional parameters of task performance, including monitoring user interaction with one more interfaces for capturing user interactions with the system.

4. The system of claim 1 wherein the system further comprises one or more feedback devices, and wherein the multimodality feedback signal activates the one or more feedback devices.

5. The system of claim 4 wherein the one or more feedback devices includes a video display.

6. The system of claim 4 wherein the one or more feedback devices includes an audio output device.

7. The system of claim 4 wherein the one or more feedback devices includes a haptic vibrator.

8. The system of claim 4 wherein the system is configured to be worn on the user'"'"'s body.

9. The system of claim 1 further comprising a wireless data interface configured to transmit psychophysiological signal data collected by the monitoring module to a remote computer station.

10. The system of claim 9 wherein the remote computer station is configured to receive psychophysiological signal data from the wireless data interface, perform offline analysis of the psychophysiological signal data, and generate performance feedback to assist the user in improving performance of the task.

11. The system of claim 9 wherein the remote computer station includes an interface for receiving feedback data from an instructor, wherein the system is configured to receive the feedback data from the system via the wireless interface, and wherein the system is configured to adjust the one or more goal states used by the expertise profiler module according to the feedback data.

12. The system of claim 1 wherein the psychophysiological signal data includes one or more of electroencephalograph (EEG) data, electrocardiograph (EKG) data, electromyography (EMG) data, respiration data, muscle tone, blood pressure, body temperature, blood flow, galvanic skin reaction, and movement data.

13. The system of claim 1, wherein the psychophysiological signal data comprises electroencephalographic activity data, and wherein computing the one or more power spectral density indices comprises:

determining one or more event-related potentials from the psychophysiological signal data, wherein each of the one or more event-related potentials represents a response to at least one task-related stimulus; and

computing the one or more power spectral density indices on one or more segments of the electroencephalographic activity data, wherein each of the one or more segments of the electroencephalographic activity data corresponds to at least one of the one or more event-related potentials centered to either a task-related stimulus, a response to a task-related stimulus, or a physiological event.

14. The system of claim 1, wherein the one or more goal states are derived from physiological measurements previously acquired from experts performing the task to be learned.

15. The system of claim 1, wherein generating the set of control signals comprises selecting one or more types of feedback from a plurality of available types of feedback based on the difference between the current psychophysiological state of the user to the one or more goal states, and generating the set of control signals for the selected one or more types of feedback.

16. The system of claim 1, wherein the tactile indication of the user'"'"'s current psychophysiological state decreases such that it stops when the user'"'"'s current psychophysiological state matches the ideal shooting state.

17. The system of claim 1, wherein the tactile indication of the user'"'"'s current psychophysiological state decreases by:

providing a first amount of haptic feedback when an alpha level of the user is at or below a baseline level; and

,

subsequently, providing at least a second amount of haptic feedback when the alpha level of the user is above the baseline level;

wherein the second amount of haptic feedback is less than the first amount of haptic feedback.

18. A method for accelerating learning of a task using real-time data acquisition and analysis of real-time psychophysiological signals obtained from a user using an adaptive portable training device worn by the user, the method comprising:

continuously monitoring at the portable training device psychophysiological signal data from a plurality of sensors attached to a user during a pre-shot period of the task to be learned; and

,

during the pre-shot period,

analyzing at the portable training device the psychophysiological signal data to generate a current psychophysiological state of the user by computing one or more power spectral density indices from electroencephalographic activity of the user and converting the one or more power spectral density indices into one or more probabilities that the user'"'"'s level of mental effort and engagement belongs to one of a plurality of classes using a discriminant function,

comparing at the portable training device the current psychophysiological state to one or more goal states associated with expert performance of the task to be learned,

determining at the portable training device multimodality feedback to be provide to the user based on the comparison of the current psychophysiological state of the user to the one or more goal states, and

delivering from the portable training device the multimodality feedback to the user in real-time to guide the user toward the one or more goal states, wherein the multimodality feedback delivered to the user is based on the control signals received from the expertise profiler module, and wherein the multimodality feedback comprises a tactile indication to the user of the user'"'"'s current psychophysiological state at a pace that replicates the user'"'"'s heartbeat, and wherein the tactile indication of the user'"'"'s current psychophysiological state decreases as the user'"'"'s current psychophysiological state approaches an ideal shooting state for the task to be learned.

19. The method of claim 18 further comprising:

continuing to monitor the psychophysiological signal data from a plurality of sensors after the feedback has been delivered to the user; and

determining whether the feedback delivered to the user has helped the user to reach the one or more goal states.

20. The method of claim 18 wherein the psychophysiological signal data includes one or more of electroencephalograph (EEG) data, electrocardiograph (EKG) data, electromyography (EMG) data, respiration data, muscle tone, blood pressure, body temperature, blood flow, galvanic skin reaction, and movement data.

21. The method of claim 18 wherein monitoring psychophysiological signal data from a plurality of sensors attached to a user further comprises:

monitoring additional parameters of task performance, including monitoring user interaction with one more interfaces for capturing user interactions with the system.

22. The method of claim 18 wherein delivering the multimodality feedback to the user to guide the user toward the one or more goal states further comprises:

generating a control signal for one or more feedback devices based on the multimodality feedback to be provided to the user.

23. The method of claim 22 wherein the one or more feedback devices includes a video display.

24. The method of claim 22 wherein the one or more feedback devices includes an audio output device.

25. The method of claim 22 wherein the one or more feedback devices includes a haptic vibrator.

26. The method of claim 22 wherein the adaptive performance trainer can be worn on the user'"'"'s body.

27. The method of claim 18 further comprising:

receiving psychophysiological signal data at a remote computer system from the adaptive performance trainer via a wireless data interface;

performing offline analysis of the psychophysiological signal data; and

generating performance feedback data to assist the user in improving performance of the task.

28. The method of claim 18 further comprising:

receiving psychophysiological signal data at a remote computer system from the adaptive performance trainer via a wireless data interface; and

receiving feedback data from an instructor at the remote computer system.

29. The method of claim 28 further comprising:

receiving at the portable training device the feedback data from the remote computer system; and

adjusting the one or more goal states used to assess progress of the user based on the feedback data.

30. The method of claim 18 wherein the accelerating learning of the task includes at least one of improving performance on the task, decreasing an amount of time to learn the task, improving the user'"'"'s retention of the task once learned, and improving the user'"'"'s ability to change from a first psychophysiological state to a second psychophysiological state during a training session.

31. The method of claim 18, wherein the psychophysiological signal data comprises electroencephalographic activity data, and wherein computing the one or more power spectral density indices comprises:

determining one or more event-related potentials from the psychophysiological signal data, wherein each of the one or more event-related potentials represents a response to at least one task-related stimulus; and

computing the one or more power spectral density indices on one or more segments of the electroencephalographic activity data, wherein each of the one or more segments of the electroencephalographic activity data corresponds to at least one of the one or more event-related potentials centered to either a task-related stimulus, a response to a task-related stimulus, or a physiological event.

32. The method of claim 18, wherein the one or more goal states are derived from physiological measurements previously acquired from experts performing the task to be learned.