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An Experimental Investigation Comparing Age-Specific and Mixed-Age Models for Wearable Assisted Activity Recognition in Women

Topics: Application of Health Informatics in Clinical Cases; Mobile Technologies for Healthcare Applications; Pattern Recognition and Machine Learning; Pervasive Health Systems and Services; Wearable Health Informatics

Authors: Pratool Bharti 1 ; Arup Dey 1 ; Sriram Chellappan 1 and Theresa Beckie 2

Affiliations: 1 Dept. of Computer Science and Engineering, University of South Florida, Tampa, FL, U.S.A. ; 2 College of Nursing, University of South Florida, Tampa, FL, U.S.A.

ISBN: 978-989-758-353-7

Keyword(s): Wearable Computing, Activity Recognition, Health Informatics, Machine Learning, Algorithms, Aging.

Abstract: In this paper, we investigate the impact of age diversity on accuracy for activity recognition among women with wrist-worn wearables. Using a sample of 10 elder women and 10 younger women, and by monitoring five activities related to cardiac care (Running, Brisk Walking, Walking, Standing and Sitting), we show that while personalized models are best, activities classification based on age specific models are definitely superior in terms of accuracy compared to classification using mixed age models. We do so by a) extracting 11 features from inertial sensing data; b) reducing dimensionality using Linear Discriminant Analysis methods; c) quantifying variance among features using Principal Component Analysis; d) clustering activities; and finally e) comparing classification accuracies of all activities for personalized, age-specific and mixed-age models. We believe that our study is unique, and potentially important for superior healthcare for women, a demographic that is largely underse rved today across the world. (More)

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Paper citation in several formats:
Bharti, P.; Dey, A.; Chellappan, S. and Beckie, T. (2019). An Experimental Investigation Comparing Age-Specific and Mixed-Age Models for Wearable Assisted Activity Recognition in Women.In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, ISBN 978-989-758-353-7, pages 367-374. DOI: 10.5220/0007398003670374

@conference{healthinf19,
author={Pratool Bharti. and Arup Kanti Dey. and Sriram Chellappan. and Theresa Beckie.},
title={An Experimental Investigation Comparing Age-Specific and Mixed-Age Models for Wearable Assisted Activity Recognition in Women},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,},
year={2019},
pages={367-374},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007398003670374},
isbn={978-989-758-353-7},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,
TI - An Experimental Investigation Comparing Age-Specific and Mixed-Age Models for Wearable Assisted Activity Recognition in Women
SN - 978-989-758-353-7
AU - Bharti, P.
AU - Dey, A.
AU - Chellappan, S.
AU - Beckie, T.
PY - 2019
SP - 367
EP - 374
DO - 10.5220/0007398003670374

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