Abstract
One of the most important functions of smart home is to monitor and assist individuals who are old or disabled. Recognizing the human activities is critical for the smart home application. In this paper, recurrent neural network (RNN) is applied to recognize the human activities. To evaluate the accuracy of the recognition algorithms, the results using real data collected from participants performing activities were assessed. With proper feature selections, the results of recurrent neural network show the significant ability to recognize human activities in smart home.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rialle V, Ollivet C, Guigui C, Herve C (2008) What do family caregivers of Alzheimer’s disease patients desire in smart home technologies? Methods Inf Med 47:63–69
Liao L, Fox D, Kautz H (2005) Location-based activity recognition using relational Markov networks. In: Proceedings of the international joint conference on artificial intelligence, 773–778
Singla G, Cook DJ, Schmitter-Edgecombe M (2010) Recognizing independent and joint activities among multiple residents in smart environments. J Ambient Intell Hum Comput 1:57–63
Yin J, Yang Q, Pan J (2008) Sensor-based abnormal human-activity detection. IEEE Trans Knowl Data Eng 20(8):1082–1090
Rabiner L (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286
Ephraim Y, Merhav N (2003) Hidden markov processes. IEEE Trans Inform Theory 48:1518–1569
van Kasteren T, Krose B (2007) Bayesian activity recognition in residence for elders. In: IET International Conference on Intelligent Environments. IE. 209–212
Cook D, Schmitter-Edgecombe M (2009) Assessing the quality of activities in a smart environment. Methods Inf Med 48(5):480–485
Zhong L (2010) Network intrusion detection method by least squares support vector machine classifier. In: The 3rd IEEE international conference on computer science and information technology, vol 2, 295–297
Jakkula VR, Crandall AS, Cook DJ (2009). Enhancing anomaly detection using temporal pattern discovery. In: Advanced intelligent environments, 175–194
Cook D, Rashidi P (2009) Keeping the resident in the loop: adapting the smart home to the user. IEEE Trans Syst, Man, Cybern, Part A 39(5):949–959
Deleawe S, Kusznir J, Lamb B, Cook D (2010) Predicting air quality in smart environments. J Ambient Intell Smart Environ 2(2):145–154
Maurer U, Smailagic A, Siewiorek D, Deisher M (2006) Activity recognition and monitoring using multiple sensors on different body positions. In: Proceedings of the international workshop on wearable and implantable body sensor networks, 99–102
Kim T (2010) Sunspot series prediction using a multiscale recurrent neural network. IEEE international symposium on signal processing and information technology. 399–403
Lee T, Ching PC, Chang LW (1998) Isolated word recognition using modular recurrent neural networks. Patten Recognition 31(6):751–760
Song HH, Kang SM, Lee SW (1996) A new recurrent neural network architecture for pattern recognition. IEEE Trans Neural Netw 8(2):331–340
Acknowledgments
This work was partially supported by Qing Lan Project, Jiangsu Province, China, and the data were collected from the smart home test-bed located on the Washington State University campus.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fang, H., Si, H., Chen, L. (2013). Recurrent Neural Network for Human Activity Recognition in Smart Home. In: Sun, Z., Deng, Z. (eds) Proceedings of 2013 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 254. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38524-7_37
Download citation
DOI: https://doi.org/10.1007/978-3-642-38524-7_37
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38523-0
Online ISBN: 978-3-642-38524-7
eBook Packages: EngineeringEngineering (R0)