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Using hierarchical clustering methods to classify motor activities of COPD patients from wearable sensor data by Delsey M Sherrill; Marilyn L Moy; John J Reilly; Paolo Bonato is a Medicine article available to read on EtoBox.

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## Background Advances in miniature sensor technology have led to the development of wearable systems that allow one to monitor motor activities in the field. A variety of classifiers have been proposed in the past, but little has been done toward developing systematic approaches to assess the feasibility of discriminating the motor tasks of interest and to guide the choice of the classifier architecture. ## Methods A technique is introduced to address this problem according to a hierarchical framework and its use is demonstrated for the application of detecting motor activities in patients with chronic obstructive pulmonary disease (COPD) undergoing pulmonary rehabilitation. Accelerometers were used to collect data for 10 different classes of activity. Features were extracted to capture essential properties of the data set and reduce the dimensionality of the problem at hand. Cluster measures were utilized to find natural groupings in the data set and then construct a hierarchy of the relationships between clusters to guide the process of merging clusters that are too similar to distinguish reliably. It provides a means to assess whether the benefits of merging for performance of

Who reads Using hierarchical clustering methods to classify motor activities of COPD patients from wearable sensor data?

Author

Delsey M Sherrill; Marilyn L Moy; John J Reilly; Paolo Bonato

Publisher

BioMed Central; Springer (Biomed Central Ltd.); [London]: BioMed Central, 2004-; Springer Science and Business Media LLC; Society for Mining, Metallurgy and Exploration Inc. (ISSN 1743-0003)

Published

2005

Language

EN

Field

Medicine (Health Sciences)