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Authors: Chinazunwa Uwaoma 1 ; Gunjan Mansingh 2 ; William Pepper 1 ; Wenshi Lu 1 and Siyu Xiang 1

Affiliations: 1 Center for Information Systems & Technology, Claremont Graduate University, 130 E 9th Street, Claremont, CA and U.S.A. ; 2 Department of Computing, The University of the West Indies, Mona, Kingston 7 and Jamaica

Keyword(s): Physical Activity, Smartphone, Respiratory Health, Signal Magnitude Area, Ambient Conditions.

Related Ontology Subjects/Areas/Topics: Aggregation, Classification and Tracking ; Data Manipulation ; Modeling, Algorithms, and Performance Evaluation ; Sensor Networks ; Sensor, Mesh and Ad Hoc Communications and Networks ; Signal Processing ; Statistical and Adaptive Signal Processing ; Telecommunications ; Wireless Information Networks and Systems

Abstract: While physical activity has been described as a primary prevention against chronic diseases, strenuous physical exertion under adverse ambient conditions has also been reported as a major contributor to exacerbation of chronic respiratory conditions. Maintaining a balance by monitoring the type and the level of physical activities of affected individuals, could help in reducing the cost and burden of managing respiratory ailments. This paper explores the potentiality of motion sensors in Smartphones to estimate physical activity thresholds that could trigger symptoms of exercise-induced respiratory conditions (EiRCs). The focus is on the extraction of measurements from the embedded motion sensors to determine the activity level and the type of activity that is tolerable to individual’s respiratory health. The calculations are based on the correlation between Signal Magnitude Area (SMA) and Energy Expenditure (EE). We also consider the effect of changes in the ambient conditions – tem perature and humidity, as contributing factors to respiratory distress during physical exercise. Real-time data collected from healthy individuals were used to demonstrate the potentiality of a mobile phone as tool to regulate the level of physical activities of individuals with EiRCs. We describe a practical situation where the experimental outcomes can be applied to promote good respiratory health. (More)

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Paper citation in several formats:
Uwaoma, C.; Mansingh, G.; Pepper, W.; Lu, W. and Xiang, S. (2019). Estimation of Physical Activity Level and Ambient Condition Thresholds for Respiratory Health using Smartphone Sensors. In Proceedings of the 9th International Conference on Pervasive and Embedded Computing and Communication Systems - PECCS; ISBN 978-989-758-385-8; ISSN 2184-2817, SciTePress, pages 113-120. DOI: 10.5220/0008170001130120

@conference{peccs19,
author={Chinazunwa Uwaoma. and Gunjan Mansingh. and William Pepper. and Wenshi Lu. and Siyu Xiang.},
title={Estimation of Physical Activity Level and Ambient Condition Thresholds for Respiratory Health using Smartphone Sensors},
booktitle={Proceedings of the 9th International Conference on Pervasive and Embedded Computing and Communication Systems - PECCS},
year={2019},
pages={113-120},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008170001130120},
isbn={978-989-758-385-8},
issn={2184-2817},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Pervasive and Embedded Computing and Communication Systems - PECCS
TI - Estimation of Physical Activity Level and Ambient Condition Thresholds for Respiratory Health using Smartphone Sensors
SN - 978-989-758-385-8
IS - 2184-2817
AU - Uwaoma, C.
AU - Mansingh, G.
AU - Pepper, W.
AU - Lu, W.
AU - Xiang, S.
PY - 2019
SP - 113
EP - 120
DO - 10.5220/0008170001130120
PB - SciTePress