Implementation of a Flexible Bayesian Classifier for the Assessment of Patient’s Activities within a Real-time Personalized Mobile Application


  • V. Miskovic Faculty of Information Technology, Slobomir P University, Bosnia and Herzegovina
  • D. Babic School of Computing, Union University, Serbia
Volume: 7 | Issue: 1 | Pages: 1405-1412 | February 2017 |


This paper presents an implementation of a mobile application that provides a real-time personalized assessment of patient’s activities by using a Flexible Bayesian Classifier. The personalized assessment is derived from data collected from the 3-axial accelerometer sensor and the counting steps sensor, both widespread among nowadays mobile devices. Despite the fact that online mobile solutions with Bayesian Classifier have been rare and insufficiently precise, we have proven that the accuracy of the proposed system within a defined data model is comparable to the accuracy of decision trees and neural networks.


activity recognition, real-time mobile application, Flexible Bayesian Classifier


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Bluetooth Medical Devices WG, “HDP Implementation Guidance White Paper”, 2009,

ZigBee Alliance, “ZigBee Wireless Sensor Applications for Health, Wellness and Fitness”, 2009,

Continua Health Alliance, “Overview Presentation: The Next Generation of Healthcare: Personal Connected Health & Wellness”, 2010,

T. Buchholz, A. Kupper, M. Schiffers, “Quality of Context: What It Is And Why We Need It”, 10th Workshop of the Open View University Association OVUA’03, Geneva, Switzerland, July, 2003

T. Garcia-Valverde, A. Muñoz, F. Arcas, A. Bueno-Crespo, A. Caballero, “Heart Health Risk Assessment System: A Nonintrusive Proposal Using Ontologies and Expert Rules”, BioMed Research International, Article ID 959645, Vol. 2014, DOI:

J. R. Kwapisz, G. M. Weiss, S. A. Moore, “Activity Recognition using Cell Phone Accelerometers”, ACM New York, Newsletter, Vol. 12, No. 2, pp. 74-82, 2010 DOI:

L. Bao, S. S. Intille, “Activity Recognition from User-Annotated Acceleration Data”, Lecture Notes in Computer Science, Vol. 3001, pp. 1-17, 2004 DOI:

M. Shoaib, S. Bosch, O. Durmaz Incel, H. Scholten, P. J. M. Havinga, “Fusion of Smartphone Motion Sensors for Physical Activity Recognition”, Sensors, Vol. 14, pp. 10146-10176, 2014 DOI:

S. Das, B. Perez , F. W. Olin, A. Perrig, “Detecting User Activities using the Accelerometer on Android Smartphones”, Journal of Multimedia and Information System Vol. 2, No 2, pp. 233-240, 2015

M. Kose, O.D. Incel, C. Ersoy, “Online human activity recognition on smart phones”, In Proceedings of the Workshop on Mobile Sensing: From Smartphones and Wearables to Big Data, Beijing, China, pp. 11–15, April 2012

M. Ziefle, C. Rocker, “Acceptance of pervasive healthcare systems: A comparison of different implementation concepts”, Pervasive Computing Technologies for Healthcare (PervasiveHealth), IEEE 4th International Conference, Munich, pp. 1-6, March 2010 DOI:

G. H. John, P. Langley, “Estimating Continuous Distributions in Bayesian Classifiers”, 11th Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers Inc. San Francisco, CA, USA, pp. 338-345, 1995

WEKA, Weka 3: Data Mining Software in Java,

T. Mitchell, Machine Learning, McGraw Hill, New York, 1997

I. Cleland, B. Kikhia, C. Nugent, A. Boytsov, J. Hallberg, K. Synnes , S. McClean, D. Finlay, “Optimal Placement of Accelerometers for the Detection of Everyday Activities”, Sensors, Vol. 13, pp. 9183-9200, 2013 DOI:

G. J. Welk, J. A. Differding, R. W. Thompson, S. N. Blair, J. Dziura, P. Hart, “The utility of the Digi-walker step counter to assess daily physical activity patterns”, Medicine & Science in Sports & Exercise, Vol. 32, pp. S481–S488, 2000 DOI:

Wikipedia, “Kernel (statistics)”,


How to Cite

V. Miskovic and D. Babic, “Implementation of a Flexible Bayesian Classifier for the Assessment of Patient’s Activities within a Real-time Personalized Mobile Application”, Eng. Technol. Appl. Sci. Res., vol. 7, no. 1, pp. 1405–1412, Feb. 2017.


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