Automatic Light Control in Domotics using Artificial Neural Networks
Home Automation is a field that, among other
subjects, is concerned with the comfort, security and energy
requirements of private homes. The configuration of automatic
functions in this type of houses is not always simple to its inhabitants
requiring the initial setup and regular adjustments. In this work, the
ubiquitous computing system vision is used, where the users- action
patterns are captured, recorded and used to create the contextawareness
that allows the self-configuration of the home automation
system. The system will try to free the users from setup adjustments
as the home tries to adapt to its inhabitants- real habits. In this paper
it is described a completely automated process to determine the light
state and act on them, taking in account the users- daily habits.
Artificial Neural Network (ANN) is used as a pattern recognition
method, classifying for each moment the light state. The work
presented uses data from a real house where a family is actually
living.
[1] M. Weiser."The Computer for the Twenty-First Century.", In Scientific
American,Sept. 1991.
[2] C. D. Kidd, R. J. Orr, G. D. Abowd, C. G. Atkeson, I. A. Essa, B.
Maclntyre, E. Mynatt, T. E. Starner and W. Newstetter. "The Aware
Home: A Living Laboratory for Ubiquitous Computing Research", Proc.
of the Second International Workshop on Cooperative Buildings, 1999.
[3] D. J. Cook, M. Youngblood, III E. 0. Heierman, K. Gopalratnam, S.
Rao, A. Litvin, and F. Khawaja, "MavHome: An agent-based
smarthome", in Proc. of First IEEE International Conference on
Pervasive Computing and Communications (PerCom'03), 2003, pp.
521-524.
[4] Philips Research - Technology. Avaliable: [August 2008],
http://www.research.philips.com/technologies/misc/homelab/index.html
[5] S. S. Intille, K. Larson, J. S. Beaudin, J. Nawyn, E. Munguia Tapia, and
P. Kaushik, "A living laboratory for the design and evaluation of
ubiquitous computing technologies", in Proc. of Conference of Human
Factors in Computing Systems, April 02-07, 2005, Portland, OR, USA,
pp.1941-1944.
[6] H. Kautz, 0. Etzioni, D. Fox, and D. Weld, "Foundations of assisted
cognition systems", University of Washington, Computer Science
Department, Technical Report, Tech. Rep., 2003.
[7] E. Munguia Tapia, S. S. Intille, and K. Larson, "Activity recognition in
the home setting using simple and ubiquitous sensors", in Proceedings
of PERVASIVE 2004, vol. LNCS 3001, A. Ferscha and F. Mattern, Eds.
Berlin Heidelberg: Springer-Verlag, 2004, pp. 158-175
[8] F. Rivera-Illingworth, V. Callaghan, and H. Hagras, "Automated
Discovery of Human Activities inside Pervasive Living Spaces", Proc.
of 1st International Symposium on Pervasive Computing and
Applications, 2006, pp. 77- 82.
[9] S. Luihr, G. West, and S. Venkatesh, "Recognition of emergent human
behaviour in a smart home: A data mining approach", Pervasive Mob.
Comput. 3, 2, pp.95-116, 2007.
[10] Huiru Zheng, Haiying Wang,Norman Black, "Human Activity Detection
in Smart Home Environment with Self-Adaptive Neural Networks", in
IEEE International Conference Networking, Sensing and Control, 2008.,
(ICNSC 2008), ISBN: 978-1-4244-1685-1, pp 1505-1510
[11] Polikar R.,"Pattern Recognition, In Wiley Encyclopedia of Biomedical
Engineering", Ed. Akay, M., New York, NY: Wiley., 2006.
[12] A. Schmidt, "Implicit Human-Computer Interaction through Context."
In International Symposium on Human Computer Interaction with
Mobile Devices and Services (MobileHCI), 1999.
[13] Carlos Machado, José Mendes, "Sensors, Actuators and Communicators
When Building a Ubiquitous System" in Proc. of IEEE ISIE 2007, in
Vigo, Spain, 2007, ISBN:1-4244-0755-9 (p1530-1535).
[14] L.A. Shalabi, Z. Shaaban, B. Kasasbeh,"Data Mining: A Preprocessing
Engine.", Journal of Computer Science 2:9 (2006) 735-735.
[15] Gurney, K. (1997),"An Introduction to Neural Networks", London:
Routledge. ISBN 1-85728-673-1
[16] Michael J. A. Berry, Gordon S. Linoff, "Data Mining Techniques: For
Marketing, Sales, and Customer Relationship Management", 2nd Ed.
ISBN: 978-0-471-47064-9
[17] Altman DG, Bland JM (1994). "Diagnostic tests. 1: Sensitivity and
specificity". BMJ 308 (6943): 1552. PMID 8019315.
[1] M. Weiser."The Computer for the Twenty-First Century.", In Scientific
American,Sept. 1991.
[2] C. D. Kidd, R. J. Orr, G. D. Abowd, C. G. Atkeson, I. A. Essa, B.
Maclntyre, E. Mynatt, T. E. Starner and W. Newstetter. "The Aware
Home: A Living Laboratory for Ubiquitous Computing Research", Proc.
of the Second International Workshop on Cooperative Buildings, 1999.
[3] D. J. Cook, M. Youngblood, III E. 0. Heierman, K. Gopalratnam, S.
Rao, A. Litvin, and F. Khawaja, "MavHome: An agent-based
smarthome", in Proc. of First IEEE International Conference on
Pervasive Computing and Communications (PerCom'03), 2003, pp.
521-524.
[4] Philips Research - Technology. Avaliable: [August 2008],
http://www.research.philips.com/technologies/misc/homelab/index.html
[5] S. S. Intille, K. Larson, J. S. Beaudin, J. Nawyn, E. Munguia Tapia, and
P. Kaushik, "A living laboratory for the design and evaluation of
ubiquitous computing technologies", in Proc. of Conference of Human
Factors in Computing Systems, April 02-07, 2005, Portland, OR, USA,
pp.1941-1944.
[6] H. Kautz, 0. Etzioni, D. Fox, and D. Weld, "Foundations of assisted
cognition systems", University of Washington, Computer Science
Department, Technical Report, Tech. Rep., 2003.
[7] E. Munguia Tapia, S. S. Intille, and K. Larson, "Activity recognition in
the home setting using simple and ubiquitous sensors", in Proceedings
of PERVASIVE 2004, vol. LNCS 3001, A. Ferscha and F. Mattern, Eds.
Berlin Heidelberg: Springer-Verlag, 2004, pp. 158-175
[8] F. Rivera-Illingworth, V. Callaghan, and H. Hagras, "Automated
Discovery of Human Activities inside Pervasive Living Spaces", Proc.
of 1st International Symposium on Pervasive Computing and
Applications, 2006, pp. 77- 82.
[9] S. Luihr, G. West, and S. Venkatesh, "Recognition of emergent human
behaviour in a smart home: A data mining approach", Pervasive Mob.
Comput. 3, 2, pp.95-116, 2007.
[10] Huiru Zheng, Haiying Wang,Norman Black, "Human Activity Detection
in Smart Home Environment with Self-Adaptive Neural Networks", in
IEEE International Conference Networking, Sensing and Control, 2008.,
(ICNSC 2008), ISBN: 978-1-4244-1685-1, pp 1505-1510
[11] Polikar R.,"Pattern Recognition, In Wiley Encyclopedia of Biomedical
Engineering", Ed. Akay, M., New York, NY: Wiley., 2006.
[12] A. Schmidt, "Implicit Human-Computer Interaction through Context."
In International Symposium on Human Computer Interaction with
Mobile Devices and Services (MobileHCI), 1999.
[13] Carlos Machado, José Mendes, "Sensors, Actuators and Communicators
When Building a Ubiquitous System" in Proc. of IEEE ISIE 2007, in
Vigo, Spain, 2007, ISBN:1-4244-0755-9 (p1530-1535).
[14] L.A. Shalabi, Z. Shaaban, B. Kasasbeh,"Data Mining: A Preprocessing
Engine.", Journal of Computer Science 2:9 (2006) 735-735.
[15] Gurney, K. (1997),"An Introduction to Neural Networks", London:
Routledge. ISBN 1-85728-673-1
[16] Michael J. A. Berry, Gordon S. Linoff, "Data Mining Techniques: For
Marketing, Sales, and Customer Relationship Management", 2nd Ed.
ISBN: 978-0-471-47064-9
[17] Altman DG, Bland JM (1994). "Diagnostic tests. 1: Sensitivity and
specificity". BMJ 308 (6943): 1552. PMID 8019315.
@article{"International Journal of Electrical, Electronic and Communication Sciences:61924", author = "Carlos Machado and José A. Mendes", title = "Automatic Light Control in Domotics using Artificial Neural Networks", abstract = "Home Automation is a field that, among other
subjects, is concerned with the comfort, security and energy
requirements of private homes. The configuration of automatic
functions in this type of houses is not always simple to its inhabitants
requiring the initial setup and regular adjustments. In this work, the
ubiquitous computing system vision is used, where the users- action
patterns are captured, recorded and used to create the contextawareness
that allows the self-configuration of the home automation
system. The system will try to free the users from setup adjustments
as the home tries to adapt to its inhabitants- real habits. In this paper
it is described a completely automated process to determine the light
state and act on them, taking in account the users- daily habits.
Artificial Neural Network (ANN) is used as a pattern recognition
method, classifying for each moment the light state. The work
presented uses data from a real house where a family is actually
living.", keywords = "ANN, Home Automation, Neural Systems, PatternRecognition, Ubiquitous Computing.", volume = "2", number = "8", pages = "1707-6", }
