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Creators/Authors contains: "Brewer, Owen"

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  1. ; ; ; ; ; (, Applied Physics Letters)
    null (Ed.)
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  2. ; ; ; ; (, IEEE Western New York Signal and Image Processing Workshop (IEEE WNYISPW 2018))
    We use commercial wearable sensors to collect three-dimensional acceleration signals from various gaits. Then, we organize the collected measurements in three-way tensors and present a simple, efficient gait classification scheme based on TUCKER2 tensor decomposition. The proposed scheme derives as multi-linear generalization of the nearest-subspace classifier. Our experimental studies show that the proposed approach manages to automatically identify the motion axes of interest and classify walking, jogging, and running gaits with high accuracy. 
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