Automatic Recognition Of Activities In Smart Environment

L. Karthik*, A.C. Sumathi**
* M.E Student CSE, Sri Shakthi Institute of Engineering and Technology, Anna University, Coimbatore.
**Assistant Professor, Department of CSE, Sri Shakthi Institute of Engineering and Technology, Anna University, Coimbatore.
Periodicity:October - December'2011
DOI : https://doi.org/10.26634/jse.6.2.2895

Abstract

Recent advancements in machine learning caused interest in the development of smart environments to emerge and assist with valuable functions such as activity monitoring and intervention. In order to monitor the activities of smart home residents, there is a need of designing technologies that recognize and track the activities that people perform at home. Machine learning techniques can perform this task, but the software algorithms rely upon large amounts of sample data that is correctly matched with the corresponding activity. The data stream captured by recording inhabitant-device interactions in an environment can be mined to discover significant patterns, which an intelligent agent could use to automate device interactions. For this, an automated approach is proposed for activity tracking that identifies frequent activities that naturally occur in an individual's routines. With this capability, the occurrences of regular activities are monitored and also can detect the changes in an individual's patterns.

Keywords

Activity Recognition, Data mining, Sequence Mining, Clustering, Smart Homes.

How to Cite this Article?

L. Karthika and A.C. Sumathi (2011). Automatic Recognition Of Activities In Smart Environment. i-manager’s Journal on Software Engineering, 6(2), 31-35. https://doi.org/10.26634/jse.6.2.2895

References

[1]. Brdiczka, O., Reignier, P., & Crowley, J.L. (2007). Detecting Individual Activities from Video in a Smart Home. Proc. 11 Int'l Conf. Knowledge-Based and Intelligent Information and Eng. Systems (KES), 363-370.
[2]. Cook, D.J., & Rashidi, P. (2009). The Resident in the Loop: Adapting the Smart Home to the User. IEEE Trans. Systems, Man, and Cybernetics, Part A: Systems and Humans, 39(5), 949-959.
[3]. Hartigan, J.A., & Wong, M.A. (1979). A K-Means Clustering Algorithm, Applied Statistics, 28, 100-108.
[4]. Levenshtein, V.L. (1966). Binary Codes Capable of Correct Deletions, Insertions, and Reversals. Soviet Physics Doklady, 10(8), 707-710.
[5]. Philipose, M., Fishkin, K.P., Perkowitz, M., Patterson, D.J., Fox, D., Kautz, H., & Hahnel, D. (2004). Inferring Activities from Interactions with Objects. IEEE Pervasive Computing, 3(4), 50-57.
[6]. Reisberg, B., Finkel, S., Overall, J., Schmidt-Gollas, N., Kanowski, S., Lehfeld, H., Hulla, F., Sclan, S.G., & Wilms, H.U. (2001). The Alzheimer's Disease Activities of Daily Living International Scale (ADL-IS). Int'l Psychogeriatrics, 13(2) 163-181.
[7]. Ruotsalainen, A., & Ala-Kleemola, T. (2007). “GAIS”: A Method for Detecting Discontinuous Sequential Patterns from Imperfect Data. Proc. Int'l Conf. Data Mining, pp. 530-534.
[8]. Tapia, E.M., Intille, S.S., & Larson, K. (2004). Activity Recognition in the Home Using Simple and Ubiquitous Sensors. Pervasive Computing, 3001, pp. 158-175.
[9]. Van Kasteren, T., & Krose, B. (2007). Bayesian Activity Recognition in Residence for Elders. Proc. Third IET Int'l Conf. Intelligent Environments, pp. 209-212.
[10]. Viterbi, A. (1967). Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm. IEEE Trans. Information Theory, IT-13(2), 260- 269.
[11]. Wadley, V.G., Okonkwo, O., Crowe, M., & Ross- Meadows, L.A. (2008). Mild Cognitive Impairment and Everyday Function: Evidence of Reduced Speed in Performing Instrumental Activities of Daily Living. Am. J. Geriatric Psychiatry, 16(5), 416-424.
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