Method and System for Hand Pose Detection
First Claim
1. A method for identification of a hand pose as input to an automated system comprising:
- providing, with a processor in the automated system, depth map data of a hand of a user to a first neural network trained to classify features corresponding to a joint angle of a wrist in the hand to generate a first plurality of activation features;
performing, with the processor and a recommendation engine stored in the memory, a first search in a predetermined plurality of activation features stored in a database in the memory to identify a first plurality of hand pose parameters for the wrist associated with predetermined activation features in the database that are nearest neighbors to the first plurality of activation features;
generating, with the processor, a hand pose model corresponding to the hand of the user based on the first plurality of hand pose parameters; and
performing, with the processor in the automated system, an operation in response to input from the user based at least in part on the hand pose model.
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Abstract
A method for hand pose identification in an automated system includes providing depth map data of a hand of a user to a first neural network trained to classify features corresponding to a joint angle of a wrist in the hand to generate a first plurality of activation features and performing a first search in a predetermined plurality of activation features stored in a database in the memory to identify a first plurality of hand pose parameters for the wrist associated with predetermined activation features in the database that are nearest neighbors to the first plurality of activation features. The method further includes generating a hand pose model corresponding to the hand of the user based on the first plurality of hand pose parameters and performing an operation in the automated system in response to input from the user based on the hand pose model.
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Citations
19 Claims
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1. A method for identification of a hand pose as input to an automated system comprising:
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providing, with a processor in the automated system, depth map data of a hand of a user to a first neural network trained to classify features corresponding to a joint angle of a wrist in the hand to generate a first plurality of activation features; performing, with the processor and a recommendation engine stored in the memory, a first search in a predetermined plurality of activation features stored in a database in the memory to identify a first plurality of hand pose parameters for the wrist associated with predetermined activation features in the database that are nearest neighbors to the first plurality of activation features; generating, with the processor, a hand pose model corresponding to the hand of the user based on the first plurality of hand pose parameters; and performing, with the processor in the automated system, an operation in response to input from the user based at least in part on the hand pose model. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A method for training a hierarchy of trained neural networks for hand pose detection comprising:
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training, with a processor, a first neural network to generate a first plurality of activation features that classify an input depth map data corresponding to a hand based on a wrist angle of the hand, the training using a plurality of depth maps of a hand with predetermined wrist angles as inputs to the first neural network during the training; and storing, with the processor, the first neural network in a memory after the training for use in classifying an additional depth map corresponding to a hand based on an angle of a wrist of the hand in the additional depth map. - View Dependent Claims (8, 9, 10, 11, 12, 13)
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14. A system for computer human interaction comprising:
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a depth camera configured to generate depth map data of a hand of a user; an output device; a memory; and a processor operatively connected to the depth camera, the output device, and the memory, the processor being configured to; receive depth map data of a hand of a user from the depth camera; provide the depth map data to a first neural network stored in the memory, the first neural network being trained to classify features corresponding to a joint angle of a wrist in the hand to generate a first plurality of activation features; perform a first search, using a recommendation engine stored in the memory, in a predetermined plurality of activation features stored in a database stored in the memory to identify a first plurality of hand pose parameters for the wrist associated with predetermined activation features in the database that are nearest neighbors to the first plurality of activation features; generate a hand pose model corresponding to the hand of the user based on the first plurality of hand pose parameters; and generate an output with the output device in response to input from the user based at least in part on the hand pose model. - View Dependent Claims (15, 16, 17, 18, 19)
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Specification