Improving Smart Lighting with Activity Recognition Using Hierarchical Hidden Markov Model
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Abstract
This paper has the aim of implementing the smart lighting systems that is able to analyze daily
movement activities, analyze the performance of hierarchical hidden markov models as predictions
and analyze the performance of smart lighting with activity analysis using hierarchical hidden
markov models. The purpose is to answer the problems that occur, namely the smart lights only turn
on if users are right under the lights so users need a smart light which is able to read the movement
of people when approaching the lamp or not. Secondly, there are also smart lights, but when users
are under the lights, it only lights up for a few seconds which should light up if there is a person
below or a radius around the lamp so that a smart light is needed when someone is underneath and
the lights will die it is outside the radius around the lamp. The model used is the hierarchical hidden
markov model which is an extension of the hidden markov model which can solve the problem of
evaluation, conclusion and learning with the algorithm used is the viterbi algorithm. The result
obtained using HHMM are accuracy of 93%, 92% recall and 86% precision.
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References
Roca, Damian, et al. "Tackling IoT Ultra Large Scale Systems: fog computing in support of hierarchical emergent behaviors." Fog
Computing in the Internet of Things. Springer, Cham, 33-48. 2018.
Jantsch, Axel, et al. "Hierarchical dynamic goal management for IoT systems." Proc. of the IEEE int. symp. on quality electronic
design, USA. Google Scholar. 2018.
Rao, Mr Pinnagadi Venkateswara, et al. "Smart Agriculture Monitoring System based on Internet of Things." 2018.
Biswas, Swagata, Ria Das, and Punyasha Chatterjee. "Energy-efficient connected target coverage in multi-hop wireless sensor
networks." Industry Interactive Innovations in Science, Engineering and Technology. Springer, Singapore, 411-421. 2018.
Cho, Ho-chan, Jong-Hyun Kim, and Y. O. U. Sung-Hee. "Methods of operation of smart lighting systems." U.S. Patent Application
No. 15/601,088.
Kandasamy, Nandha Kumar, et al. "Smart lighting system using ANN-IMC for personalized lighting control and daylight
harvesting." Building and Environment 139 (2018): 170-180.
Tang, Dalai, et al. "Evolution strategy for anomaly detection in daily life monitoring of elderly people." Society of Instrument and
Control Engineers of Japan (SICE), 2016 55th Annual Conference of the. IEEE, 2016.
Yip, Cheuk Fung, Wai Leong Ng, and Chun Yip Yau. "A hidden Markov model for earthquake prediction." Stochastic Environmental
Research and Risk Assessment 32.5 (2018): 1415-1434.
ÅžimÅŸek, A. Serdar, and Huseyin Topaloglu. "An Expectation-Maximization Algorithm to Estimate the Parameters of the Markov
Chain Choice Model." Operations Research. 2018.
Candanedo, Luis M., Véronique Feldheim, and Dominique Deramaix. "A methodology based on Hidden Markov Models for
occupancy detection and a case study in a low energy residential building." Energy and Buildings 148 (2017): 327-341.
Shen, Chao, et al. "Performance analysis of multi-motion sensor behavior for active smartphone authentication." IEEE Transactions
on Information Forensics and Security 13.1 (2018): 48-62.
Eldib, Mohamed, et al. "Behavior analysis for elderly care using a network of low-resolution visual sensors." Journal of Electronic
Imaging 25.4 (2016): 041003.
Aghdam, Mehdi Hosseinzadeh. "Context-aware recommender systems using hierarchical hidden Markov model." Physica A:
Statistical Mechanics and its Applications 518 (2019): 89-98.
Prasetyo, Muhammad Eko Budi. "Teori Dasar Hidden Markov Model." Makalah II2092 Probabilitas dan Statistik. 2010.
Irfani, Angela, Ratih Amelia, and Dyah Saptanti. "Algoritma Viterbi dalam Metode Hidden Markov Models pada Teknologi Speech
Recognition." Laboratorium Ilmu dan Rekayasa Komputasi. Departemen Teknik Informatika, Institut Teknologi Bandung (2006).
Copyright (c) 2019 Nur Ghaniaviyanto Ramadhan, Aji Gautama Putrada, Maman Abdurohman
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