EFFICIENT GESTURE PROCESSING
First Claim
1. At least one computer readable storage medium having instructions stored thereon that, when executed on a machine, cause the machine to:
- receive data from a motion sensor;
select a subset of one or more gesture recognition algorithms from a plurality of gesture recognition algorithms based, at least in part, on an amplitude of the data; and
determine a gesture from the data based, at least in part, on applying the subset of gesture recognition algorithm(s) to the data.
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Abstract
Embodiments of the invention describe a system to efficiently execute gesture recognition algorithms. Embodiments of the invention describe a power efficient staged gesture recognition pipeline including multimodal interaction detection, context based optimized recognition, and context based optimized training and continuous learning. Embodiments of the invention further describe a system to accommodate many types of algorithms depending on the type of gesture that is needed in any particular situation. Examples of recognition algorithms include but are not limited to, HMM for complex dynamic gestures (e.g. write a number in the air), Decision Trees (DT) for static poses, peak detection for coarse shake/whack gestures or inertial methods (INS) for pitch/roll detection.
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Citations
20 Claims
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1. At least one computer readable storage medium having instructions stored thereon that, when executed on a machine, cause the machine to:
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receive data from a motion sensor; select a subset of one or more gesture recognition algorithms from a plurality of gesture recognition algorithms based, at least in part, on an amplitude of the data; and determine a gesture from the data based, at least in part, on applying the subset of gesture recognition algorithm(s) to the data. - View Dependent Claims (2, 4, 5, 6, 7)
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3. The at least one computer readable storage medium 2, wherein the machine is to select the subset of gesture recognition algorithm(s) based, at least in part, on a comparison of a total energy magnitude of the data with a total energy magnitude value associated with each of the plurality of gesture algorithms.
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8. A mobile computing device comprising:
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a motion sensor; a memory; at least one processor; an algorithm selection module, stored in the memory and executed via the at least one processor, to select a subset of one or more gesture recognition algorithms from a plurality of gesture recognition algorithms based, at least in part, on an amplitude of a data from the motion sensor; and a gesture recognition module, stored in the memory and executed via the at least one processor, to determine a gesture from the data from the motion sensor based, at least in part, on applying the subset of gesture recognition algorithm(s) to the data from the motion sensor. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. A machine-implemented method comprising:
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receiving data from a motion sensor; selecting a subset of one or more gesture recognition algorithms from a plurality of gesture recognition algorithms based, at least in part, on an amplitude of the data; and determining a gesture from the data based, at least in part, on applying the subset of gesture recognition algorithm(s) to the data. - View Dependent Claims (19, 20)
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Specification