METHODS AND APPARATUS FOR INTERPERSONAL COORDINATION ANALYSIS AND TRAINING

• US 20130323698A1
• Filed: 05/15/2013
• Published: 12/05/2013
• Est. Priority Date: 05/17/2012
• Status: Active Grant

• Current Assignee: University of Connecticut (State of Connecticut)
• Original Assignee: University of Connecticut (State of Connecticut)
• Inventors: Gifford, Timothy, Bhat, Anjana, Dixon, James, Marsh, Kerry
• Granted Patent: US 9,478,147 B2
• Time in Patent Office: Days
• US Class Current: 434/247
• CPC Class Codes: G09B 19/00 Teaching not covered by other main groups of this subclass teaching or practice apparatus for gun-aiming or gun-laying F41G3/26

Abstract

Methods and systems for interpersonal coordination analysis and training are provided. In an example embodiment of a method for interpersonal coordination analysis and training, at least one physical action is selected to be performed. A leader subject is instructed to perform the at least one physical action in the presence of at least one follower subject. The at least one physical action of the leader subject is tracked. The at least one follower subject is instructed to mimic the at least one physical action of the leader subject. The at least one physical action of the at least one follower subject is tracked. Tracking data obtained from the tracking of the leader subject and the at least one follower subject is analyzed. From the analyzing of the tracking data, a responsiveness of the at least one follower subject to mimic the leader subject can be determined.

Claims

1. A method for interpersonal coordination analysis and training, comprising:

selecting at least one physical action to be performed;

instructing a leader subject to perform said at least one physical action in the presence of at least one follower subject;

tracking said at least one physical action of said leader subject;

instructing said at least one follower subject to mimic said at least one physical action of said leader subject;

tracking the at least one physical action of said at least one follower subject;

analyzing tracking data obtained from the tracking of the leader subject and the at least one follower subject; and

determining, from the analyzing of the tracking data, a responsiveness of said at least one follower subject to mimic said leader subject.

2. The method of claim 1, wherein:

the leader subject repeatedly performs, and the at least one follower subject in turn repeatedly mimics, one of the at least one physical actions; and

the tracking data is continuously analyzed and the responsiveness is continuously determined during interactions between the leader and the at least one follower subject.

3. The method of claim 2, further comprising:

altering said at least one physical action of said leader subject based on said responsiveness.

4. The method of claim 3, wherein said altering comprises a role reversal between the leader subject and the at least one follower subject.

5. The method of claim 3, wherein said altering comprises at least one of:

changing a frequency of said at least one physical action;

changing a sequence of movements of said at least one physical action; and

changing said at least one physical action.

6. The method of claim 5, wherein:

the tracking data is recorded as at least one time series;

the responsiveness is determined for each of the at least one time series.

7. The method of claim 6, wherein:

interpersonal synchrony between the leader and each of the at least one follower subject is determined using Multifractal Detrended Fluctuation Analysis;

Hurst exponents are computed for each scale of the at least one time series for the leader subject and the follower subject;

a first vector of Hurst exponents is computed for the leader subject;

a second vector of Hurst exponents is computed for the follower subject;

vector autoregression and vector error-correction models are used to predict future activity of the follower subject.

8. The method of claim 1, wherein said leader subject is a robot and said at least one follower subject is human.

9. The method of claim 1, wherein said leader subject is human and said at least one follower subject comprises a robot.

10. The method of claim 1, wherein said selecting of said at least one physical action is performed by said leader subject.

11. The method of claim 1, wherein said selecting at least one physical action is performed by an independent operator and instructions for said at least one physical action are provided to said leader subject by said operator.

12. The method of claim 11, wherein said operator comprises a human attendant.

13. The method of claim 12, wherein in addition to the tracking data, data is manually provided by the operator and used in the determining of the responsiveness.

14. The method of claim 1, wherein said tracking includes position detection using position detection devices comprising at least one of (i) time-of-flight cameras, and (ii) electromagnetic position and orientation trackers mounted on at least one of the leader subject and the follower subject.

15. The method of claim 1, wherein said responsiveness is used to assess a level of interpersonal synchrony and coordination exhibited by the at least one follower subject.

16. The method of claim 15, wherein techniques to determine said responsiveness comprise at least one of Cross-Recurrence Quantification Analysis (CRQA), Multifractal Detrended Fluctuation Analysis (MFDFA), and epoching.

17. The method of claim 1, wherein said interpersonal coordination analysis is applied to therapeutic treatment of human cognitive development disorders.

18. The method of claim 1, wherein said the analyzing of the tracking data occurs in real-time.

19. The method of claim 1, wherein the at least one physical action comprises a series of rhythmic actions.

20. A system for interpersonal coordination analysis and training, comprising:

at least one tracking device for tracking physical action;

at least one processing device; and

a robot capable of mimicking a plurality of human physical actions;

wherein;

the processing device is adapted for analyzing tracking data from said at least one tracking device and determining responsiveness of a human subject during interaction with said robot; and

said responsiveness is used to assess interpersonal synchrony and coordination of said human subject.