Issue

Vol. 5, No. 1, February 2020

Context-Aware Smart Door Lock with Activity Recognition Using Hierarchical Hidden Markov Model

https://doi.org/10.22219/kinetik.v5i1.904
Aji Gautama Putrada
Telkom University
Nur Ghaniaviyanto Ramadhan
Telkom University
Maman Abdurohman
Telkom University

Corresponding Author(s) : Aji Gautama Putrada

ajigps@telkomuniversity.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 5, No. 1, February 2020

Abstract

Context-Aware Security demands a security system such as a Smart Door Lock to be flexible in determining security levels. The context can be in various forms; a person’s activity in the house is one of them and is proposed in this research. Several learning methods, such as Naïve Bayes, have been used previously to provide context-aware security systems, using related attributes. However conventional learning methods cannot be implemented directly to a Context-Aware system if the attribute of the learning process is low level. In the proposed system, attributes are in forms of movement data obtained from a PIR Sensor Network. Movement data is considered low level because it is not related directly to the desired context, which is activity. To solve the problem, the research proposes a hierarchical learning method, namely Hierarchical Hidden Markov Model (HHMM). HHMM will first transform the movement data into activity data through the first hierarchy, hence obtaining high level attributes through Activity Recognition. The second hierarchy will determine the security level through the activity pattern. To prove the success rate of the proposed method a comparison is made between HHMM, Naïve Bayes, and HMM. Through experiments created in a limited area with real sensed activity, the results show that HHMM provides a higher F1-Measure than Naïve Bayes and HMM in determining the desired context in the proposed system. Besides that, the accuracies obtained respectively are 88% compared to 75% and 82%.

Keywords

Context-Aware Smart Door Lock Activity Recognition Hierarchical Hidden Markov Model Wireless Sensor Network
Putrada, A. G., Ramadhan, N. G., & Abdurohman, M. (2020). Context-Aware Smart Door Lock with Activity Recognition Using Hierarchical Hidden Markov Model. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 5(1), 37-44. https://doi.org/10.22219/kinetik.v5i1.904
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Author Biographies

Aji Gautama Putrada, Telkom University
Department of Informatics
Nur Ghaniaviyanto Ramadhan, Telkom University
Department of Informatics
Maman Abdurohman, Telkom University
Department of Informatics
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