System for detection of body motion
First Claim
1. A method for classifying aggregated data in a distributed sensor system, comprising:
- obtaining sensor data from a plurality of sensors at a local site;
performing a local classification of the sensor data using local processing, the local processing comprising;
taking projections of the sensor data to reduce dimensionality;
calculating sparse representations of features using training sequences;
validating local measurements;
classifying valid local measurements; and
transmitting classified valid measurements to a network base station.
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Accused Products
Abstract
Methods for classifying aggregated data in a distributed sensor system are provided and illustrated with a wearable motion sensor network. The classification is operated in a distributed fashion on individual sensor nodes and a base station computer. The method classifies actions using a set of training motion sequences as prior examples and may reject outlying actions that are not in the training categories. Acquired sensor data is processed at the node by taking projections of the data to reduce dimensionality, calculating sparse representations of features using training sequences; validating and classifying local measurements and then transmitting classified measurements to a network base station. The base station aggregates local sensor measurements and performs a global classification of the data by forming global features from the local measurements; calculating sparse representations of global features; validating and classifying valid global features; and labeling global features and their corresponding local features.
26 Citations
12 Claims
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1. A method for classifying aggregated data in a distributed sensor system, comprising:
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obtaining sensor data from a plurality of sensors at a local site; performing a local classification of the sensor data using local processing, the local processing comprising; taking projections of the sensor data to reduce dimensionality; calculating sparse representations of features using training sequences; validating local measurements; classifying valid local measurements; and transmitting classified valid measurements to a network base station. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method for classifying aggregated data in a distributed sensor system, comprising:
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obtaining sensor data from a plurality of sensors at a local site; performing a local classification of the sensor data with local processing, the local processing comprising; taking projections of the obtained sensor data to reduce dimensionality; calculating sparse representations of measurements using training sequences; validating local measurements; and labeling valid local measurements; transmitting labeled valid local measurements from a local processor to a base station receiver; and performing a global classification of the labeled sensor data using global processing with a base station processor, the global processing comprising; forming global features from valid local measurements from a plurality of sensors; calculating sparse representations of global features; validating global features; classifying valid global features; and labeling valid global features and corresponding local features.
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12. A distributed sensor system, comprising:
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a plurality of sensor nodes, each node having a node processor, a plurality of sensors and a transceiver; a non-transitory processor-readable medium including one or more instructions for; obtaining sensor data from a plurality of sensors at a local site; performing a local classification of the sensor data with local processing on the node processor, the local processing comprising; taking projections of the sensor data to reduce dimensionality; providing training sequences for each sensor; calculating sparse representations of features using training sequences; validating local measurements; and classifying valid local measurements; a base station with a station processor and a transceiver; and a non-transitory processor-readable medium including one or more instructions for; obtaining labeled sensor data transmitted from a plurality of sensor nodes; performing a global classification of the sensor data with the station processor, the global processing comprising; forming global features from valid local measurements from a plurality of sensors; calculating sparse representations of global features; validating global features; and classifying valid global features; and labeling valid global features and corresponding local features.
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Specification