System and method for automatically defining and identifying a gesture
First Claim
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1. A method comprising:
- recording test sequences of one or more users performing examples of sequences;
recording a training set of gestures;
marking the training set of gestures by;
marking examples of a correct performance of the gesture as positive examples; and
marking examples of an incorrect performance of the gesture as negative examples; and
applying a machine learning algorithm to the marked training set to generate a classifier to determine if movements not included in the training set of gestures is similar to a positive example;
testing the classifier on the test sequences to detect a predetermined number of false positives and false negatives, wherein the test sequences are distinct from the training set of gestures.
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Abstract
A system and method for creating a gesture and generating a classifier that can identify the gesture for use with an application is described. The designer constructs a training set of data containing positive and negative examples of the gesture. Machine learning algorithms are used to compute the optimal classification of the training data into positive and negative instances of the gesture. The machine learning algorithms generate a classifier which, given input data, makes a decision on whether the gesture was performed in the input data or not.
83 Citations
30 Claims
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1. A method comprising:
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recording test sequences of one or more users performing examples of sequences; recording a training set of gestures; marking the training set of gestures by; marking examples of a correct performance of the gesture as positive examples; and marking examples of an incorrect performance of the gesture as negative examples; and applying a machine learning algorithm to the marked training set to generate a classifier to determine if movements not included in the training set of gestures is similar to a positive example; testing the classifier on the test sequences to detect a predetermined number of false positives and false negatives, wherein the test sequences are distinct from the training set of gestures. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method comprising:
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recording test sequences of one or more users performing examples of sequences; prompting a user via a graphical user interface (GUI) to perform a training gesture; recording a plurality of performances of the training gesture by the user; applying a machine learning algorithm to the plurality of performances to train a classifier for the gesture recognition model; and testing the classifier on the test sequences to detect a predetermined number of false positives and false negatives, wherein the test sequences are distinct from the training set of gestures. - View Dependent Claims (12, 13, 14, 15)
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16. An apparatus for generating a gesture recognition model for determining if a gesture has been performed by one of a plurality of users, the apparatus comprising:
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a recording module to record sequences of one or more users performing examples of sequences and a training set of gestures; a gesture information module to receive information associated with the gestures recorded in the training set from a designer; a marking module to mark the training set of gestures by marking examples of a correct performance of the gesture as positive examples and marking examples of an incorrect performance of the gesture as negative examples a machine learning module to apply a machine learning algorithm to generate a classifier to determine if movements not included in the training set of gestures is similar to a positive example; and a testing module to test the classifier on the test sequences to detect a predetermined number of false positives and false negatives, wherein the test sequences are distinct from the training set of gestures. - View Dependent Claims (17, 18, 19, 20, 21)
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22. An apparatus for generating a gesture recognition model for determining if a gesture has been performed by a user, the apparatus comprising:
- a recording module configured to record test sequences of one or more users performing examples of sequences;
a graphical user interface (GUI) to prompt a user to perform a training gesture; a recording module configured to record sequences of one or more users performing examples of sequences and a plurality of performances of the training gesture by the user; a machine learning module configured to apply a machine learning algorithm to the plurality of performances to train a classifier for the gesture recognition model; and a testing module to test the classifier on the test sequences to detect a predetermined number of false positives and false negatives, wherein the test sequences are distinct from the training set of gestures. - View Dependent Claims (23, 24)
- a recording module configured to record test sequences of one or more users performing examples of sequences;
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25. A non-transitory computer readable medium having instructions, which when executed by a processor, cause the processor to form operations comprising:
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recording test sequences of one or more users performing examples of sequences; recording a training set of gestures; marking the training set of gestures by; marking examples of a correct performance of the gesture as positive examples; and marking examples of an incorrect performance of the gesture as negative examples; and applying a machine learning algorithm to the marked training set to generate a classifier to determine if movements not included in the training set of gestures is similar to a positive example; testing the classifier on the test sequences to detect a predetermined number of false positives and false negatives, wherein the test sequences are distinct from the training set of gestures. - View Dependent Claims (26, 27)
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28. A non-transitory computer readable medium having instructions, which when executed by a processor, cause the processor to form operations comprising:
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recording test sequences of one or more users performing examples of sequences; prompting a user via a graphical user interface (GUI) to perform a training gesture; recording a plurality of performances of the training gesture by the user; applying a machine learning algorithm to the plurality of performances to train a classifier for the gesture recognition model; and testing the classifier on the test sequences to detect a predetermined number of false positives and false negatives, wherein the test sequences are distinct from the training set of gestures. - View Dependent Claims (29, 30)
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Specification