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Motion primitive-based human activity recognition using a bag-of-features approach

Published: 28 January 2012 Publication History

Abstract

Human activity modeling and recognition using wearable sensors is important in pervasive healthcare, with applications including quantitative assessment of motor function, rehabilitation, and elder care. Previous human activity recognition techniques use a "whole-motion" model in which continuous sensor streams are divided into windows with a fixed time duration whose length is chosen such that all the relevant information in each activity signal can be extracted from each window. In this paper, we present a statistical motion primitive-based framework for human activity representation and recognition. Our framework is based on Bag-of-Features (BoF), which builds activity models using histograms of primitive symbols. We experimentally validate the effectiveness the BoF-based framework for recognizing nine activity classes and evaluate six factors which impact the performance of the framework. The factors include window size, choices of features, methods to construct motion primitives, motion vocabulary size, weighting schemes of motion primitive assignments, and learning machine kernel functions. Finally, we demonstrate that our statistical BoF-based framework can achieve much better performance compared to a non-statistical string-matching-based approach.

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cover image ACM Conferences
IHI '12: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
January 2012
914 pages
ISBN:9781450307819
DOI:10.1145/2110363
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 28 January 2012

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Author Tags

  1. bag-of-features
  2. human activity recognition
  3. motion primitives
  4. pattern recognition
  5. pervasive healthcare
  6. wearable sensing technologies

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IHI '12
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IHI '12: ACM International Health Informatics Symposium
January 28 - 30, 2012
Florida, Miami, USA

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  • (2024)Learning Motion Primitives for the Quantification and Diagnosis of Mobility DeficitsIEEE Transactions on Biomedical Engineering10.1109/TBME.2024.340435771:12(3339-3349)Online publication date: Dec-2024
  • (2024)Role of the Cerebellum in the Construction of Functional and Geometrical SpacesThe Cerebellum10.1007/s12311-024-01693-yOnline publication date: 16-Apr-2024
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