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Computational Balancing between Wearable Sensor and Smartphone towards Energy-Efficient Remote Healthcare MonitoringSECERBEGOVIC, A.   , GOGIC, A. , SULJANOVIC, N. , ZAJC, M. , MUJCIC, A. |
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Author keywords
wearable sensors, mobile computing, body sensor networks, biomedical signal processing, performance evaluation
References keywords
mobile(7), computing(6), sensor(5), wearable(4), time(4), systems(4), selection(4), recognition(4), real(4), feature(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2018-11-30
Volume 18, Issue 4, Year 2018, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.04001
Web of Science Accession Number: 000451843400001
SCOPUS ID: 85058816909
Abstract
Recent advances in the development of wearable sensors and smartphones open up opportunities for executing computing operations on the devices instead of using them for streaming raw data. By minimizing power consumption due to the wireless transmission, limited energy resources of wearable devices can be utilized not only for sensing, but also for processing physiological signals. Computational tasks between a wearable sensor and a smartphone can be distributed efficiently in order to provide balance between power consumption of both processing and transmission of the data. In this paper, we have analyzed the computational balancing between a wearable sensor and a smartphone. Presented models show different trade-offs between classification accuracy, processing time and power consumption due to different number and types of extracted features and classification models. Our results are based on a physiological dataset, where electrocardiogram and electro dermal activity signals were collected from 24 individuals in short-term stress and mental workload detection scenario. Our findings show that placing a feature extraction on a wearable sensor is efficient when processing cost of the extracted features is small. On the other hand, moving classification task to the smartphone can improve accuracy of recognition without compromising the overall power consumption. |
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| [1] M. Patel and J, Wang, "Applications, challenges, and prospective in emerging body area networking technologies", IEEE Wireless communications, vol. 17, no. 1, pp. 80-88, 2010. [CrossRef] [Web of Science Times Cited 408] [SCOPUS Times Cited 570] [2] U. Varshney, "Pervasive Healthcare and Wireless Health Monitoring", Mobile Network Applications, vol. 12, pp. 113-127, 2007. [CrossRef] [Web of Science Times Cited 349] [SCOPUS Times Cited 565] [3] T. Rault, A. Bouabdallah, Y. Challal and F. Marin, "A survey of energy-efficient context recognition systems using wearable sensors for healthcare applications", Pervasive and Mobile Computing, vol. 37, pp. 23-44, 2017. [CrossRef] [Web of Science Times Cited 72] [SCOPUS Times Cited 97] [4] H. Ghasemzadeh, N. Amini, R. Saeedi and M. Sarrafzadeh, "Power-aware computing in wearable sensor networks: An optimal feature selection", IEEE Transactions on Mobile Computing, vol. 14, no. 4, pp. 800-812, 2015. [CrossRef] [Web of Science Times Cited 62] [SCOPUS Times Cited 77] [5] K. Kumar, and Y.H. Lu, "Cloud computing for mobile users: Can offloading computation save energy?", Computer, vol. 43, no. 4, pp. 51-56, 2010. [CrossRef] [6] C. Ragona, F. Granelli, C. Fiandrino, D. Kliazovich and P. Bouvry. "Energy-efficient computation offloading for wearable devices and smartphones in mobile cloud computing", In Global Communications Conference (GLOBECOM), 2015 IEEE, pp. 1-6. IEEE, 2015. [CrossRef] [Web of Science Times Cited 53] [7] H. Kalantarian, C. Sideris, B. Mortazavi, N. Alshurafa and M. Sarrafzadeh, "Dynamic computation offloading for low-power wearable health monitoring systems", IEEE Transactions on Biomedical Engineering, vol. 64, no. 3, pp. 621-628, 2017. [CrossRef] [Web of Science Times Cited 34] [SCOPUS Times Cited 47] [8] R. Braojos, I. Beretta, J. Constantin, A. Burg and D. Atienza, "A wireless body sensor network for activity monitoring with low transmission overhead," in 12th IEEE Int Conf EUC, pp. 265-272, 2014. [CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 14] [9] T. Park, J. Lee, I. Hwang, C. Yoo, L. Nachman and J. Song," E-Gesture: a collaborative architecture for energy-efficient gesture recognition with hand-worn sensor and mobile devices", 9th ACM Conference on Embedded Networked Sensor Systems, pp. 260-273, 2011. [CrossRef] [SCOPUS Times Cited 89] [10] L. Wang, T. Gu, X. Tao and J. Lu, "A hierarchical approach to real-time activity recognition in body sensor networks", Pervasive and Mobile Computing, vol. 8, no. 1, pp. 115-130, 2012. [CrossRef] [Web of Science Times Cited 79] [SCOPUS Times Cited 108] [11] I. Guyon, and A. Elisseeff, "An introduction to variable and feature selection", Journal of machine learning research, no. 3, pp. 1157-1182, 2003. [12] V. Bolon-Canedo, I. Porto-Diaz, N. Sanches-Marono and A. Alonso-Betanzos, "A framework for cost-based feature selection", Pattern recognition, vol. 47, no. 7, pp. 2481-2489, 2014. [CrossRef] [Web of Science Times Cited 57] [SCOPUS Times Cited 68] [13] [Online] Available: Temporary on-line reference link removed - see the PDF document [14] [Online] Available: Temporary on-line reference link removed - see the PDF document [15] H. Peng, F. Long and C. Ding, "Feature selection based on mutual information criteria of max-dependency, max-relevance and min-redundancy", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp.1226-1238, 2005. [CrossRef] [Web of Science Times Cited 7157] [SCOPUS Times Cited 8586] [16] J. A. Healey and R. W. Picard., "Detecting stress during real-world driving tasks using physiological sensors", IEEE Transactions on intelligent transportation systems, vol. 6, no. 2, pp. 156-166, 2005. [CrossRef] [Web of Science Times Cited 1257] [SCOPUS Times Cited 1606] [17] O. Grigore and L-V. Bornoiu, "Kohonen Neural Network Stress Detection Using Only Electrodermal Activity Features", Advances in Electrical and Computer Engineering, vol. 14, no. 3, pp. 71-78, 2014. [CrossRef] [Full Text] [Web of Science Times Cited 9] [SCOPUS Times Cited 10] [18] I.H. Witten, E. Frank, E., M. A. Hall and C. J. Pal, "Data Mining: Practical machine learning tools and techniques", Morgan Kaufmann, 2016 [19] R. A. Lockhart, "Interrelations between amplitude, latency, rise time, and the Edelberg recovery measure of the galvanic skin response", Psychophysiology, vol. 9, no. 4, pp.437-442, 1972. [CrossRef] [SCOPUS Times Cited 33] [20] H.C, Chen and S. W. Chen, "A moving average based filtering system with its application to real-time QRS detection", Computers in Cardiology, pp. 585-588, 2003. [CrossRef] [SCOPUS Times Cited 99] [21] J. Pan and W.J. Tompkins, "A real-time QRS detection algorithm", IEEE Transactions on Biomedical Engineering, vol. 23, pp. 230-236, 1985. [CrossRef] [Web of Science Times Cited 4984] [SCOPUS Times Cited 6214] [22] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I. H. Witten, "The weka data mining software: an update," ACM SIGKDD explorations newsletter, vol. 11, no. 1, pp. 10-18, 2009. [CrossRef] Web of Science® Citations for all references: 14,529 TCR SCOPUS® Citations for all references: 18,183 TCR Web of Science® Average Citations per reference: 632 ACR SCOPUS® Average Citations per reference: 791 ACR TCR = Total Citations for References / ACR = Average Citations per Reference We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more Citations for references updated on 2024-11-16 00:57 in 122 seconds. Note1: Web of Science® is a registered trademark of Clarivate Analytics. Note2: SCOPUS® is a registered trademark of Elsevier B.V. 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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
