Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Publisher: IEEE
Languages: English
Types: Part of book or chapter of book
Analysis of the office workers’ activities of daily working in an intelligent office environment can be used to optimize energy consumption and also office workers’ comfort. To achieve this end, it is essential to recognise office workers’ activities including short breaks, meetings and non-computer activities to allow an optimum control strategy to be implemented. In this paper, fuzzy finite state machines are used to model an office worker’s behaviour. The model will incorporate sensory data collected from the environment as the input and some pre-defined fuzzy states are used to develop the model. Experimental results are presented to illustrate the effectiveness of this approach. The activity models of different individual workers as inferred from the sensory devices can be distinguished. However, further investigation is required to create a more complete model.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] N. K. Suryadevara, A. Gaddam, R. K. Rayudu, and S. C. Mukhopadhyay, “Wireless sensors network based safe home to care elderly people: Behaviour detection”, Sensors and Actuators, vol. 186, pp. 277-283, 10 2012.
    • [2] Saifullizam Puteh, Caroline Langensiepen, and Ahmad Lotfi, “Fuzzy ambient intelligence for intelligent office environments”, in Proc. of IEEE International Conference on Fuzzy Systems, 2012, pp. 1-6.
    • [3] Liming Chen, J. Hoey, C. D. Nugent, D. J. Cook, and Zhiwen Yu, “Sensor-based activity recognition”, IEEE Trans. on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 42, no. 6, pp. 790-808, 2012.
    • [4] Vincent Tabak and Bauke de Vries, “Methods for the prediction of intermediate activities by office occupants”, Building and Environment, vol. 45, no. 6, pp. 1366-1372, 2010.
    • [5] Xin Hong and C. D. Nugent, “Partitioning time series sensor data for activity recognition”, in Proc. of 9th International Conference on Information Technology and Applications in Biomedicine, 2009, pp. 1-4.
    • [6] Saisakul Chernbumroong, Shuang Cang, Anthony Atkins, and Hongnian Yu, “Elderly activities recognition and classification for applications in assisted living”, Expert Systems with Applications, vol. 40, no. 5, pp. 1662-1674, 4 2013.
    • [7] Hamid Medjahed, Dan Istrate, Jerome Boudy, and Bernadette Dorizzi, “Human activities of daily living recognition using fuzzy logic for elderly home monitoring”, in Proc. of the International Conference on Fuzzy Systems, Republic of Korea, August 2009, pp. 2001-2006.
    • [8] D. Naranjo-Hernandez, L. M. Roa, J. Reina-Tosina, and M. A. Estudillo-Valderrama, “Som: A smart sensor for human activity monitoring and assisted healthy ageing”, IEEE Transactions on Biomedical Engineering, vol. 59, no. 11, pp. 3177-3184, 2012.
    • [9] R. R. Fletcher et al, “icalm: Wearable sensor and network architecture for wirelessly communicating and logging autonomic activity”, Information Technology in Biomedicine, IEEE Transactions on, vol. 14, no. 2, pp. 215-223, 2010.
    • [10] Venet Osmani, Sasitharan Balasubramaniam, and Dmitri Botvich, “Human activity recognition in pervasive health-care: Supporting efficient remote collaboration”, Journal of Network and Computer Applications, vol. 31, no. 4, pp. 628-655, 11 2008.
    • [11] P. Rashidi, D.J. Cook, L.B. Holder, and M. SchmitterEdgecombe, “Discovering activities to recognize and track in a smart environment”, IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 4, pp. 527 -539, april 2011.
    • [12] M. Ros, M. Delgado, and A. Vila, “A system to supervise behaviours using temporal and sensor information”, in Proc. of IEEE Int. Conf. on Fuzzy Systems, 2010, pp. 1-8.
    • [13] H. Madokoro, K. Honma, and K. Sato, “Classification of behavior patterns with trajectory analysis used for event site”, in Proc. of International Joint Conference on Neural Networks (IJCNN), 2012, pp. 1-8.
    • [14] T. Martin, B. Majeed, Beum-Seuk Lee, and N. Clarke, “Fuzzy ambient intelligence for next generation telecare”, in Proc. of IEEE International Conference on Fuzzy Systems, 2006, pp. 894-901.
    • [15] B. S. Lee, T. P. Martin, N. P. Clarke, B. Majeed, and D. Nauck, “Dynamic daily-living patterns and association analyses in telecare systems”, in Proc. of the 4th IEEE Int. Conf. on Data Mining (ICDM), 2004, pp. 447-450.
    • [16] M. Ahad, J. K. Tan, H. S. Kim, and S. Ishikawa, “Human activity recognition: Various paradigms”, in Proc. of Int. Conf. on Control, Automation and Systems (ICCAS), 2008, pp. 1896- 1901.
    • [17] Sung-Ihk Yang and Sung-Bae Cho, “Recognizing human activities from accelerometer and physiological sensors”, in Proc. of IEEE Int. Conf. on Multisensor Fusion and Integration for Intelligent Systems (MFI), 2008, pp. 100-105.
    • [18] Zhelong Wang, Ming Jiang, Yaohua Hu, and Hongyi Li, “An incremental learning method based on probabilistic neural networks and adjustable fuzzy clustering for human activity recognition by using wearable sensors”, IEEE Trans. on Information Tech. in Biomedicine, vol. 16, no. 4, pp. 691-699, 2012.
    • [19] L.M. Reyneri, “An introduction to fuzzy state automata”, in Biological and Artificial Computation: From Neuroscience to Technology, Jos Mira, Roberto Moreno-Daz, and Joan Cabestany, Eds., vol. 1240 of Lecture Notes in Computer Science, pp. 273- 283. Springer Berlin Heidelberg, 1997.
    • [20] A. Alvarez-Alvarez, G. Trivino, and O. Cordon, “Human gait modeling using a genetic fuzzy finite state machine”, IEEE Transactions on Fuzzy Systems, vol. 20, no. 2, pp. 205-223, 2012.
    • [21] S. Puteh, C. Langensiepen, and A. Lotfi, “Fuzzy ambient intelligence for intelligent office environments”, in Proc. of IEEE Int. Conf. on Fuzzy Systems, 2012, pp. 1-6.
    • [22] Kim Eunju, S. Helal, and D. Cook, “Human activity recognition and pattern discovery”, IEEE Pervasive Computing, vol. 9, no. 1, pp. 48-53, 2010.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article