Authors:
Pratool Bharti
1
;
Arup Kanti Dey
1
;
Sriram Chellappan
1
and
Theresa Beckie
2
Affiliations:
1
Dept. of Computer Science and Engineering, University of South Florida, Tampa, FL and U.S.A.
;
2
College of Nursing, University of South Florida, Tampa, FL and U.S.A.
Keyword(s):
Wearable Computing, Activity Recognition, Health Informatics, Machine Learning, Algorithms, Aging.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Biomedical Signal Processing
;
Devices
;
Distributed and Mobile Software Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Human-Computer Interaction
;
Mobile Technologies
;
Mobile Technologies for Healthcare Applications
;
Neural Rehabilitation
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition and Machine Learning
;
Pervasive Health Systems and Services
;
Physiological Computing Systems
;
Software Engineering
;
Wearable Sensors and Systems
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 unders
erved today across the world.
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