Method and system of group interaction by user state detection

1. A method for detecting interaction between two or more participants in a meeting, the method comprising:

capturing at least one three-dimensional (3D) stream of data on the two or more participants;

extracting a time-series of skeletal data from the at least one 3D stream of data on the two or more participants;

classifying the time-series of skeletal data for each of the two or more participants based on a plurality of body position classifiers;

capturing an audio stream of data on the two or more participants;

classifying the audio stream of data on the two more participants into an utterance classifier based on utterances detected on the audio stream of data, the utterance classifier including at least one of identification of pitch of speech, frequency of speech, and volume of speech;

calculating an engagement score for each of the two or more participants based on the classifying of the time-series of skeletal data and the utterance classifier based on utterances detected on the audio stream for each of the two or more participants; and

providing a feedback in accordance with at least one of the engagement scores of the two or more participants.

2. The method of claim 1 , comprising:

capturing a weight stream of data on the two or more participants, the weight stream of data corresponding to a weight distribution for each of the two or more participants on a chair received from one or more sensors in the chair; and

adding a weight distribution classifier to the engagement score based the weight distribution for each of the two or more participants.

3. The method of claim 1 , comprising:

generating a group engagement score for the two or more participants; and

generating feedback based on the engagement score for each of the two or more participants and the group engagement score, the feedback including one or more of the following:

audio, visual, vibration alert by means of a wearable device, or a change of environmental conditions in a meeting room.

4. The method of claim 1 , comprising:

applying a finite state machine model to the time-series of skeletal data for each of the two or more participants.

5. The method of claim 1 , comprising:

determining trends in meeting between the two or more participants based on the engagement scores for the two or more participants and a group engagement score; and

adjusting meeting times, participants, and/or interaction protocols based on the determined trends.

6. A method for detecting interaction between two or more participants in a meeting, the method comprising:

capturing at least one three-dimensional (3D) stream of data on the two or more participants;

extracting a time-series of skeletal data from the at least one 3D stream of data on the two or more participants;

classifying the time-series of skeletal data for each of the two or more participants based on a plurality of body position classifiers;

calculating an engagement score for each of the two or more participants based on the classifying of the time-series of skeletal data or each of the two or more participants;

providing a feedback in accordance with at least one of the engagement scores of the two or more participants; and

wherein the classifying of the time-series of skeletal data for each of the two or more participants based on the plurality of body position classifiers comprises:

applying a Maximum Mean Discrepancy (MMD) criterion to the time-series of skeletal data to detect change-points over continuous gestures as an initial estimated cuts of gesture transitions;

using kinematic constraints to revise an initial estimated cut to an accurate gesture transition position; and

using a probability density estimation to estimate a hand motion between two cuts to eliminate unintentional movements and non-gesture segments.

7. A non-transitory computer readable medium containing a computer program storing computer readable code for detecting interaction between two or more participant, the program being executable by a computer to cause the computer to perform a process comprising:

capturing at least one three-dimensional (3D) stream of data on the two or more participants;

extracting a time-series of skeletal data from the at least one 3D stream of data on the two or more participants;

classifying the time-series of skeletal data for each of the two or more participants based on a plurality of body position classifiers;

capturing an audio stream of data on the two or more participants;

classifying the audio stream of data on the two more participants into an utterance classifier based on utterances detected on the audio stream of data, the utterance classifier including at least one of identification of pitch of speech, frequency of speech, and volume of speech;

calculating an engagement score for each of the two or more participants based on the classifying of the time-series of skeletal data and the utterance classifier based on utterances detected on the audio stream for each of the two or more participants; and

providing a feedback in accordance with at least one of the engagement scores of the two or more participants.

8. The non-transitory computer readable medium of claim 7 , comprising:

capturing a weight stream of data on a weight distribution of the two or more participants, the weight stream of data corresponding to a weight distribution for each of the two or more participants on a chair received from one or more sensors in the chair; and

adding a weight distribution classifier to the engagement score based the weight distribution for each of the two or more participants.

9. A method for improving a group interaction in a meeting in which two or more participants participate in, the method comprising:

calculating, for each of the two or more participants, an individual engagement state based on attitudes of the participant, wherein the individual engagement state is an engagement state of the participant to the meeting including an engaged state and a disengaged state, wherein the individual engagement state includes:

extracting a time-series of skeletal data from the at least one 3D stream of data on each of the two or more participants;

classifying the time-series of skeletal data for the two or more participants based on a plurality of body position classifiers;

capturing an audio stream of data on the two or more participants;

classifying the audio stream of data on the two more participants into an utterance classifier based on utterances detected on the audio stream of data, the utterance classifier including at least one of identification of pitch of speech, frequency of speech, and volume of speech; and

calculating the individual engagement state for each of the two or more participants based on the classifying of the time-series of skeletal data and the utterance classifier based on utterances detected on the audio stream for each of the two or more participants;

calculating a group engagement state based on the attitudes of the two or more participants, wherein the group engagement state is an engagement state of the two or more participants to the meeting; and

providing a feedback, comprising:

proving a group warning to all of the participants regardless of their individual engagements states, if a number of participants having the disengaged state exceeds a first threshold;

providing a warning only to the participants who have the disengaged state, if the number of the participants who have the disengaged state does not exceed a second threshold; and

providing an environmental feedback to a meeting room system in accordance with the group engagement state.

10. The method as claimed in claim 9 , wherein the feedback is provided based on the attitudes of the participants that are made during a certain time frame.

11. The method as claimed in claim 9 , wherein the first threshold is higher than the second threshold.

12. The method as claimed in claim 9 , wherein an environmental feedback to support the meeting is also provided if the number of participants having the disengaged state exceeds the first threshold.

13. The method as claimed in claim 9 , wherein environmental feedback is provided when changes in the group engagement state is greater than a third threshold.

14. The method of claim 9 , further comprising:

capturing a weight stream of data on the two or more participants, the weight stream of data corresponding to a weight distribution for each of the two or more participants on a chair received from one or more sensors in the chair; and

adding a weight distribution classifier to the individual engagement state based the weight distribution for each of the two or more participants.

15. A non-transitory computer readable medium containing a computer program storing computer readable code for improving a group interaction in a meeting in which two or more participants participate in, the program being executable by a computer to cause the computer to perform a process comprising:

calculating, for each of the two or more participants, an individual engagement state based on attitudes of the participant, wherein the individual engagement state is an engagement state of the participant to the meeting including an engaged state and a disengaged state, wherein the individual engagement state includes:

extracting a time-series of skeletal data from the at least one 3D stream of data on each of the two or more participants;

classifying the time-series of skeletal data for the two or more participants based on a plurality of body position classifiers;

capturing an audio stream of data on the two or more participants;

classifying the audio stream of data on the two more participants into an utterance classifier based on utterances detected on the audio stream of data, the utterance classifier including at least one of identification of pitch of speech, frequency of speech, and volume of speech; and

calculating the individual engagement state for each of the two or more participants based on the classifying of the time-series of skeletal data and the utterance classifier based on utterances detected on the audio stream for each of the two or more participants;

calculating a group engagement state based on the attitudes of the two or more participants, wherein the group engagement state is an engagement state of the two or more participants to the meeting; and

providing a feedback, comprising:

proving a group warning to all of the participants regardless of their individual engagements states, if a number of participants having the disengaged state exceeds a first threshold;

providing a warning only to the participants who have the disengaged state, if the number of the participants who have the disengaged state does not exceed a second threshold; and

providing an environmental feedback to a meeting room system in accordance with the group engagement state.

16. The non-transitory computer readable medium of claim 15 , wherein the feedback is provided based on the attitudes of the participants that are made during a certain time frame.

17. The non-transitory computer readable medium of claim 15 , wherein the first threshold is higher than the second threshold.

18. The non-transitory computer readable medium of claim 15 , wherein an environmental feedback to support the meeting is also provided if the number of participants having the disengaged state exceeds the first threshold.

19. The non-transitory computer readable medium of claim 15 , wherein environmental feedback is provided when changes in the group engagement state is greater than a third threshold.

20. The non-transitory computer readable medium of claim 15 , further comprising:

capturing a weight stream of data on the two or more participants, the weight stream of data corresponding to a weight distribution for each of the two or more participants on a chair received from one or more sensors in the chair; and

adding a weight distribution classifier to the individual engagement state based the weight distribution for each of the two or more participants.