Feature Dimension Reduction of Multisensor Data Fusion using Principal Component Fuzzy Analysis

Authors

Department of Computer Science & Software Engineering, Ferdows Islamic Azad University, Ferdows, Iran

Abstract

These days, the most important areas of research in many different applications, with different tools, are focused on how to get awareness. One of the serious applications is the awareness of the behavior and activities of patients. The importance is due to the need of ubiquitous medical care for individuals. That the doctor knows the patient's physical condition, sometimes is very important. Of course, there are other important applications for this information. There are a variety of methods and tools for measurement, gathering, and analysis of the physical behaviors and activities’ information. One of the most successful tools for this aim are ubiquitous intelligent electronic devices, specifically smartphones, and smart watches. There are many sensors in these devices, some of which can be used to understand the activities of daily living. As an output result, these sensors produce many raw data. Thus, it is needed to process these information and recognize the individual behavior of the output of this processing. In this paper, the basic components of the analysis phase for this process have been proposed. Simulations validate the benefits and superiority of this method.

Keywords

References

1.     Favell, A., "“Global mobile statistics 2014 part a: Mobile subscribers; handset market share; mobile operators", mobiThinking, (2014).
2.     Kearney, A., "The mobile economy 2013", London: GSMA,  (2013).
3.     Sanou, B., "The world in 2013: Ict facts and figures", International Telecommunications Union,  (2013).
4.     Bianchi, F., Redmond, S.J., Narayanan, M.R., Cerutti, S. and Lovell, N.H., "Barometric pressure and triaxial accelerometry-based falls event detection", IEEE Transactions on Neural Systems and Rehabilitation Engineering,  Vol. 18, No. 6, (2010), 619-627.
5.     Silva, M., Teixeira, P.M., Abrantes, F. and Sousa, F., "Design and evaluation of a fall detection algorithm on mobile phone platform", in International Conference on Ambient Media and Systems, Springer., (2011), 28-35.
6.     Zhang, T., Wang, J., Liu, P. and Hou, J., "Fall detection by embedding an accelerometer in cellphone and using kfd algorithm", International Journal of Computer Science and Network Security,  Vol. 6, No. 10, (2006), 277-284.
7.     Sposaro, F. and Tyson, G., "Ifall: An android application for fall monitoring and response", in Engineering in Medicine and Biology Society,. EMBC. Annual International Conference of the, IEEE., (2009), 6119-6122.
8.     Hausdorff, J.M., Rios, D.A. and Edelberg, H.K., "Gait variability and fall risk in community-living older adults: A 1-year prospective study", Archives of Physical Medicine and Rehabilitation,  Vol. 82, No. 8, (2001), 1050-1056.
9.     Menz, H.B., Lord, S.R. and Fitzpatrick, R.C., "Acceleration patterns of the head and pelvis when walking are associated with risk of falling in community-dwelling older people", The Journals of Gerontology Series A: Biological Sciences and Medical Sciences,  Vol. 58, No. 5, (2003), M446-M452.
10.   Bieber, G., Koldrack, P., Sablowski, C., Peter, C. and Urban, B., "Mobile physical activity recognition of stand-up and sit-down transitions for user behavior analysis", in Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments, ACM., (2010), 50-59.
11.   Bieber, G., Voskamp, J. and Urban, B., "Activity recognition for everyday life on mobile phones", in International Conference on Universal Access in Human-Computer Interaction, Springer., (2009), 289-296.
12.   Yang, J., "Toward physical activity diary: Motion recognition using simple acceleration features with mobile phones", in Proceedings of the 1st international workshop on Interactive multimedia for consumer electronics, ACM., (2009), 1-10.
13.   Sun, L., Zhang, D. and Li, N., "Physical activity monitoring with mobile phones", in International Conference on Smart Homes and Health Telematics, Springer., (2011), 104-111.
14.   Terrier, P. and Schutz, Y., "How useful is satellite positioning system (GPS) to track gait parameters? A review", Journal of Neuroengineering and Rehabilitation,  Vol. 2, No. 1, (2005), 28.
15.   Parkka, J., Ermes, M., Korpipaa, P., Mantyjarvi, J., Peltola, J. and Korhonen, I., "Activity classification using realistic data from wearable sensors", IEEE Transactions on Information Technology in Biomedicine,  Vol. 10, No. 1, (2006), 119-128.
16.   Ganti, R.K., Srinivasan, S. and Gacic, A., "Multisensor fusion in smartphones for lifestyle monitoring", in Body Sensor Networks (BSN), International Conference on, IEEE., (2010), 36-43.
17.   Morón, M.J., Luque, R. and Casilari, E., "On the capability of smartphones to perform as communication gateways in medical wireless personal area networks", Sensors,  Vol. 14, No. 1, (2014), 575-594.
18.   Moshiri, B., Asharif, M.R. and Hoseinnezhad, R., "A new approach to self-localization for mobile robots using sensor data fusion", International Journal of Engineering Transactions B,  Vol. 15, (2002), 145-156.
19.   Moussavi Khalkhali, A., Moshiri, B. and Momeni, H., "Designing a home security system using sensor data fusion with dst and dsmt methods", International Journal of Engineering-Transactions A: Basics,  Vol. 22, No. 1, (2008), 13.
20.   Han, M., Lee, Y.-K. and Lee, S., "Comprehensive context recognizer based on multimodal sensors in a smartphone", Sensors,  Vol. 12, No. 9, (2012), 12588-12605.
21.   Godfrey, A., Conway, R., Meagher, D. and ÓLaighin, G., "Direct measurement of human movement by accelerometry", Medical Engineering & Physics,  Vol. 30, No. 10, (2008), 1364-1386.
22.   Moe-Nilssen, R., "A new method for evaluating motor control in gait under real-life environmental conditions. Part 1: The instrument", Clinical Biomechanics,  Vol. 13, No. 4-5, (1998), 320-327.
23.   Staudenmayer, J., Pober, D., Crouter, S., Bassett, D. and Freedson, P., "An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer", Journal of Applied Physiology,  Vol. 107, No. 4, (2009), 1300-1307.
24.   Kwapisz, J.R., Weiss, G.M. and Moore, S.A., "Activity recognition using cell phone accelerometers", ACM SigKDD Explorations Newsletter,  Vol. 12, No. 2, (2011), 74-82.
25.   Maurer, U., Smailagic, A., Siewiorek, D.P. and Deisher, M., "Activity recognition and monitoring using multiple sensors on different body positions", in Wearable and Implantable Body Sensor Networks,. BSN. International Workshop on, IEEE., (2006), 4 pp.-116.
26.   Guiry, J.J., van de Ven, P. and Nelson, J., "Multi-sensor fusion for enhanced contextual awareness of everyday activities with ubiquitous devices", Sensors,  Vol. 14, No. 3, (2014), 5687-5701.
27.   Esmaileyan, Z. and Marvi, H., "A database for automatic persian speech emotion recognition: Collection, processing and evaluation", International Journal of Engineering,  Vol. 27, No., (2013), 79-90.
28.   Srikanth, S., Sudha, K. and Raju, Y.B., "Fuzzy load frequency controller in deregulated power environment by principal component analysis".
29.   Dehghan, H., Pouyan, A.A. and Hassanpour, H., "Detection of alzheimer's disease using multitracer positron emission tomography imaging", International Journal of Engineering, Transactions A: Basics,  Vol. 27, No. 1, (2014), 51-56.
30.   AhilaPriyadharshini, R. and Arivazhagan, S., "Object recognition based on local steering kernel and svm", International Journal of Engineering-Transactions B: Applications,  Vol. 26, No. 11, (2013), 1281-1288.
31.   Hamidi, H. and Daraee, A., "Analysis of pre-processing and post-processing methods and using data mining to diagnose heart diseases", International Journal of Engineering-Transactions A: Basics,  Vol. 29, No. 7, (2016), 921.
32.   Marsland, S., "Machine learning: An algorithmic perspective, CRC press,  (2015).
International Journal of Engineering
Volume 30, Issue 4
TRANSACTIONS A: Basics
April 2017
Pages 493-499

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