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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.

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First Claim