@inproceedings{a60aacd678db4a2f94ea0bc5bc8fb015,
title = "Using Hidden Markov Model to Predict Human Actions with Swarm Intelligence",
abstract = "This paper proposed a novel algorithm which named Randomized Particle Swarm Optimization (RPSO) to optimize HMM for human activity prediction. The experiments designed in this paper are the classification of human activity using two data sets. The first testing data is from the TUM Kitchen Data Set and the other is the Human Activity Recognition using the Smartphone Data Set from UCI Machine Learning Repository. Based on the comparison of the accuracies for the conventional HMM and optimized HMM, a conclusion can be drawn that the proposed RPSO can help HMM to achieve higher accuracy for human action recognition. Our results show that RPSO-HMM can improve 15% accuracy in human activity recognition and prediction when compared to the traditional HMM.",
keywords = "Hidden Markov Model, Human activity prediction, Particle Swarm Optimization",
author = "Zhicheng Lu and Chung, {Yuk Ying} and Yeung, {Henry Wing Fung} and Zandavi, {Seid Miad} and Weiming Zhi and Yeh, {Wei Chang}",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 24th International Conference on Neural Information Processing, ICONIP 2017 ; Conference date: 14-11-2017 Through 18-11-2017",
year = "2017",
doi = "10.1007/978-3-319-70093-9_3",
language = "English",
isbn = "9783319700922",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag Italia Srl",
pages = "21--30",
editor = "Derong Liu and Shengli Xie and Yuanqing Li and El-Alfy, {El-Sayed M.} and Dongbin Zhao",
booktitle = "Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings",
address = "Italy",
}