University of California

University of Illinois

Allen Yang Yang

Roozbeh Jafari

Ruzena Bajcsy

S Shankar Sastry

Yi Ma

John Norbert Wright

Andrew William Wagner

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.

System for detection of body motion

A method is disclosed for recognition of high-dimensional data in the presence of occlusion, including: receiving a target data that includes an occlusion and is of an unknown class, wherein the target data includes a known object; sampling a plurality of training data files comprising a plurality of distinct classes of the same object as that of the target data; and identifying the class of the target data through linear superposition of the sampled training data files using lminimization, wherein a linear superposition with a sparsest number of coefficients is used to identify the class of the target data.

Recognition via high-dimensional data classification

Growing community of inventors

Berkeley, CA, United States of America

Allen Yang Yang