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Authors: Anzah H. Niazi 1 ; Delaram Yazdansepas 1 ; Jennifer L. Gay 1 ; Frederick W. Maier 1 ; Lakshmish Ramaswamy 1 ; Khaled Rasheed 1 and Matthew Buman 2

Affiliations: 1 University of Georgia, United States ; 2 Arizona State University, United States

Keyword(s): Actigraph g3x+, Analysis of Variance, Body-worn Accelerometers, Data Mining, Human Activity Recognition, Random Forests, Sampling Rate, Weighted Least Squares, WEKA, Window Size.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Data Mining ; Databases and Information Systems Integration ; Devices ; Distributed and Mobile Software Systems ; Enterprise Information 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 ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Software Engineering ; Wearable Sensors and Systems

Abstract: Accelerometers are the most common device for data collection in the field of Human Activity Recognition (HAR). This data is recorded at a particular sampling rate and then usually separated into time windows before classification takes place. Though the sampling rate and window size can have a significant impact on the accuracy of the trained classifier, there has been relatively little research on their role in activity recognition. This paper presents a statistical analysis on the effect the sampling rate and window sizes on HAR data classification. The raw data used in the analysis was collected from a hip-worn Actigraphy G3X+ at 100Hz from 77 subjects performing 23 different activities. It was then re-sampled and divided into windows of varying sizes and trained using a single data classifier. A weighted least squares linear regression model was developed and two-way factorial ANOVA was used to analyze the effects of sampling rate and window size for different activity types and demographic categories. Based upon this analysis, we find that 10-second windows recorded at 50Hz perform statistically better than other combinations of window size and sampling rate. (More)

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Paper citation in several formats:
Niazi, A.; Yazdansepas, D.; Gay, J.; Maier, F.; Ramaswamy, L.; Rasheed, K. and Buman, M. (2017). Statistical Analysis of Window Sizes and Sampling Rates in Human Activity Recognition. In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - HEALTHINF; ISBN 978-989-758-213-4; ISSN 2184-4305, SciTePress, pages 319-325. DOI: 10.5220/0006148503190325

@conference{healthinf17,
author={Anzah H. Niazi. and Delaram Yazdansepas. and Jennifer L. Gay. and Frederick W. Maier. and Lakshmish Ramaswamy. and Khaled Rasheed. and Matthew Buman.},
title={Statistical Analysis of Window Sizes and Sampling Rates in Human Activity Recognition},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - HEALTHINF},
year={2017},
pages={319-325},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006148503190325},
isbn={978-989-758-213-4},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - HEALTHINF
TI - Statistical Analysis of Window Sizes and Sampling Rates in Human Activity Recognition
SN - 978-989-758-213-4
IS - 2184-4305
AU - Niazi, A.
AU - Yazdansepas, D.
AU - Gay, J.
AU - Maier, F.
AU - Ramaswamy, L.
AU - Rasheed, K.
AU - Buman, M.
PY - 2017
SP - 319
EP - 325
DO - 10.5220/0006148503190325
PB - SciTePress