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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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wearable sensors, mobile computing, body sensor networks, biomedical signal processing, performance evaluation
mobile(7), computing(6), sensor(5), wearable(4), time(4), systems(4), selection(4), recognition(4), real(4), feature(4)
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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
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.
|References|||||Cited By «-- Click to see who has cited this paper|
| 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 342]
 U. Varshney, "Pervasive Healthcare and Wireless Health Monitoring", Mobile Network Applications, vol. 12, pp. 113-127, 2007.
[CrossRef] [Web of Science Times Cited 301]
 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 37]
 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 45]
 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.
 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 40]
 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 15]
 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 7]
 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.
 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 61]
 I. Guyon, and A. Elisseeff, "An introduction to variable and feature selection", Journal of machine learning research, no. 3, pp. 1157-1182, 2003.
 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 32]
 [Online] Available: Temporary on-line reference link removed - see the PDF document
 [Online] Available: Temporary on-line reference link removed - see the PDF document
 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 4592]
 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 743]
 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 3]
 I.H. Witten, E. Frank, E., M. A. Hall and C. J. Pal, "Data Mining: Practical machine learning tools and techniques", Morgan Kaufmann, 2016
 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.
 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.
 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 3266]
 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.
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