System and method for recognizing user-specified pen-based gestures using hidden markov models
First Claim
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1. A method adapted for use in a system for recognizing user specified gestures, the method comprising steps of:
- a) initiating a training mode of the system;
b) specifying a gesture class to which a first single gesture belongs, a gesture class comprising at least one subclass of gestures;
c) inputting the first gesture to the system;
d) training a first sample Hidden Markov Model (HMM) on the first gesture, the first sample HMM being classified in the at least one subclass that belongs to the specified gesture class;
e) inputting a subsequent gesture to the system, the subsequent gesture belonging to the specified gesture class;
f) training a subsequent sample HMM on the subsequent gesture;
g) comparing the subsequent sample HMM to a threshold HMM to determine therebetween a first distance;
h) comparing the subsequent sample HMM to one of an HMM and HMMs of the at least one subclass belonging to the specified gesture class to determine therebetween one of a second distance and second distances;
i) selecting a single HMM of the one of HMM and HMMs of the at least one subclass corresponding to a shortest second distance;
j) merging the subsequent sample HMM with the selected single HMM if the first distance is greater than the second distance whereby the selected single HMM is modified;
k) creating a new subclass, comprising the subsequent sample HMM, if the first distance is less than the shortest second distance; and
, l) performing steps e) through k) for subsequent gestures belonging to the specified gesture class that are input to the system.
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Abstract
A method for recognizing user specified pen-based gestures uses Hidden Markov Models. A gesture recognizer is implemented which includes a fast pruning procedure. In addition, an incremental training method is utilized.
120 Citations
11 Claims
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1. A method adapted for use in a system for recognizing user specified gestures, the method comprising steps of:
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a) initiating a training mode of the system;
b) specifying a gesture class to which a first single gesture belongs, a gesture class comprising at least one subclass of gestures;
c) inputting the first gesture to the system;
d) training a first sample Hidden Markov Model (HMM) on the first gesture, the first sample HMM being classified in the at least one subclass that belongs to the specified gesture class;
e) inputting a subsequent gesture to the system, the subsequent gesture belonging to the specified gesture class;
f) training a subsequent sample HMM on the subsequent gesture;
g) comparing the subsequent sample HMM to a threshold HMM to determine therebetween a first distance;
h) comparing the subsequent sample HMM to one of an HMM and HMMs of the at least one subclass belonging to the specified gesture class to determine therebetween one of a second distance and second distances;
i) selecting a single HMM of the one of HMM and HMMs of the at least one subclass corresponding to a shortest second distance;
j) merging the subsequent sample HMM with the selected single HMM if the first distance is greater than the second distance whereby the selected single HMM is modified;
k) creating a new subclass, comprising the subsequent sample HMM, if the first distance is less than the shortest second distance; and
,l) performing steps e) through k) for subsequent gestures belonging to the specified gesture class that are input to the system. - View Dependent Claims (2)
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3. A method adapted for use in a system for recognizing user specified gestures wherein a specified gesture class has been initialized to include at least one single input gesture, the method comprising steps of:
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a) inputting a subsequent gesture to the system, the subsequent gesture belonging to the specified gesture class;
b) training a subsequent sample HMM on the subsequent gesture;
c) comparing the subsequent sample HMM to a threshold HMM to determine therebetween a first distance;
d) comparing the subsequent sample HMM to an HMM or HMMs of at least one subclass belonging to the specified gesture class to determine therebetween a second distance or distances;
e) selecting a single HMM of the HMMs of the at least one subclass corresponding to a shortest second distance;
f) merging the subsequent sample HMM with the selected single HMM if the first distance is greater than the second distance whereby the selected single HMM is modified; and
,g) creating a new subclass, comprising the subsequent sample HMM, if the first distance is less than the shortest second distance. - View Dependent Claims (4, 5)
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6. A method adapted for use in a system for recognizing user specified gestures, the method comprising steps of:
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inputting a single gesture;
training a sample Hidden Markov Model (HMM) on the single gesture;
comparing the sample HMM to a threshold HMM to determine therebetween a first distance;
sequentially comparing the sample HMM to a plurality of HMMs to determine respective second distances between the sample HMM and each of the plurality, each of the plurality representing a subclass of gestures;
selecting HMMs of the plurality that correspond to second distances that are less than the first distance;
computing the probability of the gesture relative to the selected HMMs of the plurality; and
,determining a subclass to which the gesture most likely belongs based on the computing.
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7. A method adapted for use in a system for recognizing user specified gestures, the method comprising steps of:
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inputting a single gesture;
training a sample Hidden Markov Model (HMM) on the single gesture;
comparing the sample HMM to a threshold HMM to obtain first data;
sequentially comparing the sample HMM to a plurality of HMMs to determine second data;
selecting HMMs of the plurality based on the first and second data;
computing the probability of the gesture relative to the selected HMMs of the plurality; and
,determining a subclass to which the gesture most likely belongs based on the computing.
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8. A system for recognizing user specified gestures, the system comprising:
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means for initiating a training mode of the system;
means for specifying a gesture class to which a first single gesture belongs, a gesture class comprising at least one subclass of gestures;
means for inputting the first gesture to the system;
means for training a first sample Hidden Markov Model (HMM) on the first gesture, the first sample HMM being classified in the at least one subclass that belongs to the specified gesture class;
means for inputting a subsequent gesture to the system, the subsequent gesture belonging to the specified gesture class;
means for training a subsequent sample HMM on the subsequent gesture;
means for comparing the subsequent sample HMM to a threshold HMM to determine therebetween a first distance;
means for comparing the subsequent sample HMM to an HMM or HMMs of the at least one subclass belonging to the specified gesture class to determine therebetween a second distance or distances;
means for selecting a single HMM of the HMM of the at least one subclass corresponding to a shortest second distance;
means for merging the subsequent sample HMM with the selected single HMM if the first distance is greater than the second distance whereby the selected single HMM is modified; and
means for creating a new subclass, comprising the subsequent sample HMM, if the first distance is less than the shortest second distance.
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9. A system for recognizing user specified gestures wherein a specified gesture class has been initialized to include at least one single input gesture, the system comprising:
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means for inputting a subsequent gesture to the system, the subsequent gesture belonging to the specified gesture class;
means for training a subsequent sample HMM on the subsequent gesture;
means for comparing the subsequent sample HMM to a threshold HMM to determine therebetween a first distance;
means for comparing the subsequent sample HMM to an HMM or HMMs of at least one subclass belonging to the specified gesture class to determine therebetween a second distance or distances;
means for selecting a single HMM of the HMM of the at least one subclass corresponding to a shortest second distance;
means for merging the subsequent sample HMM with the selected single HMM if the first distance is greater than the second distance whereby the selected single HMM is modified; and
,means for creating a new subclass, comprising the subsequent sample HMM, if the first distance is less than the shortest second distance.
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10. A system for recognizing user specified gestures, the system comprising:
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means for inputting a single gesture;
means for training a sample Hidden Markov Model (HMM) on the single gesture;
means for comparing the sample HMM to a threshold HMM to determine therebetween a first distance;
means for sequentially comparing the sample HMM to a plurality of HMMs to determine respective second distances between the sample HMM and each of the plurality, each of the plurality representing a subclass of gestures;
means for selecting HMMs of the plurality that correspond to second distances that are less than the first distance;
means for computing the probability of the gesture relative to the selected HMMs of the plurality; and
,means for determining a subclass to which the gesture most likely belongs based on the computing.
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11. A system for recognizing user specified gestures, the system comprising:
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means for inputting a single gesture;
means for training a sample Hidden Markov Model (HMM) on the single gesture;
means for comparing the sample HMM to a threshold HMM to obtain first data;
means for sequentially comparing the sample HMM to a plurality of HMMs to determine second data;
means for selecting HMMs of the plurality based on the first and second data;
means for computing the probability of the gesture relative to the selected HMMs of the plurality; and
,means for determining a subclass to which the gesture most likely belongs based on the computing.
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Specification