Journal Home > Volume 28 , Issue 5

Human Activity Recognition (HAR) has become a subject of concern and plays an important role in daily life. HAR uses sensor devices to collect user behavior data, obtain human activity information and identify them. Markov Logic Networks (MLN) are widely used in HAR as an effective combination of knowledge and data. MLN can solve the problems of complexity and uncertainty, and has good knowledge expression ability. However, MLN structure learning is relatively weak and requires a lot of computing and storage resources. Essentially, the MLN structure is derived from sensor data in the current scene. Assuming that the sensor data can be effectively sliced and the sliced data can be converted into semantic rules, MLN structure can be obtained. To this end, we propose a rulebase building scheme based on probabilistic latent semantic analysis to provide a semantic rulebase for MLN learning. Such a rulebase can reduce the time required for MLN structure learning. We apply the rulebase building scheme to single-person indoor activity recognition and prove that the scheme can effectively reduce the MLN learning time. In addition, we evaluate the parameters of the rulebase building scheme to check its stability.


menu
Abstract
Full text
Outline
About this article

Generating Markov Logic Networks Rulebase Based on Probabilistic Latent Semantics Analysis

Show Author's information Shan Cui1Tao Zhu2Xiao Zhang1Liming Chen3Lingfeng Mao1Huansheng Ning1( )
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Computer School, University of South China, Hengyang 421001, China
School of Computing, Ulster University, Belfast, BT15 1AP, UK

Abstract

Human Activity Recognition (HAR) has become a subject of concern and plays an important role in daily life. HAR uses sensor devices to collect user behavior data, obtain human activity information and identify them. Markov Logic Networks (MLN) are widely used in HAR as an effective combination of knowledge and data. MLN can solve the problems of complexity and uncertainty, and has good knowledge expression ability. However, MLN structure learning is relatively weak and requires a lot of computing and storage resources. Essentially, the MLN structure is derived from sensor data in the current scene. Assuming that the sensor data can be effectively sliced and the sliced data can be converted into semantic rules, MLN structure can be obtained. To this end, we propose a rulebase building scheme based on probabilistic latent semantic analysis to provide a semantic rulebase for MLN learning. Such a rulebase can reduce the time required for MLN structure learning. We apply the rulebase building scheme to single-person indoor activity recognition and prove that the scheme can effectively reduce the MLN learning time. In addition, we evaluate the parameters of the rulebase building scheme to check its stability.

Keywords: structure learning, Markov Logic Network (MLN), rulebase construction, probabilistic latent semantics

References(23)

[1]
D. Triboan, L. M. Chen, F. Chen, and Z. Wang, A semantics-based approach to sensor data segmentation in real-time Activity Recognition, Future Gener. Comput. Syst., vol. 93, pp. 224–236, 2019.
[2]
S. Kok and P. Domingos, Learning the structure of Markov logic networks, in Proc. 22nd Int. Conf. on Machine Learning, Bonn, Germany, 2005, pp. 441–448.
[3]
P. Domingos and D. Lowd, Markov Logic: An Interface Layer for Artificial Intelligence. San Rafael, CA, USA: Morgan & Claypool, 2009.
DOI
[4]
P. Chahuara, A. Fleury, F. Portet, and M. Vacher, Using Markov logic network for on-line activity recognition from non-visual home automation sensors, in Proc. 3rd Int. Joint Conf. on Ambient Intelligence, Pisa, Italy, 2012, pp. 177–192.
[5]
I. Chakraborty, A. Elgammal, and R. S. Burd, Video based activity recognition in trauma resuscitation, in Proc. 10th IEEE Int. Conf. and Workshops on Automatic Face and Gesture Recognition, Shanghai, China, 2013, pp. 1–8.
[6]
K. S. Gayathri, S. Elias, and B. Ravindran, Hierarchical activity recognition for dementia care using Markov logic network, Pers. Ubiquit. Comput., vol. 19, no. 2, pp. 271–285, 2015.
[7]
K. S. Gayathri, K. S. Easwarakumar, and S. Elias, Probabilistic ontology based activity recognition in smart homes using Markov logic network, Knowl.-Based Syst., vol. 121, pp. 173–184, 2017.
[8]
Y. Honda, H. Yamaguchi, and T. Higashino, Daily activity recognition based on Markov logic network for elderly monitoring, in Proc. 16th IEEE Annu. Consumer Communications & Networking Conf., Las Vegas, NV, USA, 2019, pp. 1–6.
[9]
S. Cui, T. Zhu, X. Zhang, and H. Ning, MCLA: Research on cumulative learning of Markov logic network, Knowl.-Based Syst., vol. 242, pp. 108352, 2022.
[10]
T. Zhang and C. C. J. Kuo, Audio content analysis for online audiovisual data segmentation and classification, IEEE Trans. Speech Audio Process., vol. 9, no. 4, pp. 441–457, 2001.
[11]
K. Iqbal, M. Isa, S. A. Buzdar, K. A. Gifford, and M. Afzal, Treatment planning evaluation of sliding window and multiple static segments technique in intensity modulated radiotherapy, Rep. Pract. Oncol. Radiother., vol. 18, no. 2, pp. 101–106, 2013.
[12]
G. Okeyo, L. Chen, H. Wang, and R. Sterritt, Dynamic sensor data segmentation for real-time knowledge-driven activity recognition, Pervasive Mob. Comput., vol. 10, pp. 155–172, 2014.
[13]
S. L. Chua, S. Marsland, and H. W. Guesgen, Spatio-temporal and context reasoning in smart homes, in Proc. COSIT Workshop on Spatial and Temporal Reasoning for Ambient Intelligence System, Freiburg, Germany, 2009, pp. 9–20.
[14]
D. Riboni, L. Pareschi, L. Radaelli, and C. Bettini, Is ontology-based activity recognition really effective? in Proc. 2011 IEEE Int. Conf. on Pervasive Computing and Communications Workshops, Seattle, WA, USA, 2011, pp. 427–431.
[15]
H. Cho, J. An, I. Hong, and Y. Lee, Automatic sensor data stream segmentation for real-time activity prediction in smart spaces, in Proc. 2015 Workshop on IoT Challenges in Mobile and Industrial Systems, Florence, Italy, 2015, pp. 13–18.
[16]
E. M. Tapia, S. S. Intille, and K. Larson, Activity recognition in the home using simple and ubiquitous sensors, in Proc. 2nd Int. Conf. on Pervasive Computing, Vienna, Austria, 2004, pp. 158–175.
[17]
X. Hong, C. Nugent, M. Mulvenna, S. McClean, B. Scotney, and S. Devlin, Evidential fusion of sensor data for activity recognition in smart homes, Pervasive Mob. Comput., vol. 5, no. 3, pp. 236–252, 2009.
[18]
J. Y. Yang, J. S. Wang, and Y. P. Chen, Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers, Pattern Recognit. Lett., vol. 29, no. 16, pp. 2213–2220, 2008.
[19]
D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-Ortiz, Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine, in Proc. 4th Int. Workshop on Ambient Assisted Living, Vitoria-Gasteiz, Spain, 2012, pp. 216–223.
[20]
M. Qi, J. Qin, A. Li, Y. Wang, J. Luo, and L. Van Gool, stagNet: An attentive semantic RNN for group activity recognition, in Proc. 15th European Conf. on Computer Vision, Munich, Germany, 2018, pp. 104–120.
[21]
Z. Wang, M. Jiang, Y. Hu, and H. Li, An incremental learning method based on probabilistic neural networks and adjustable fuzzy clustering for human activity recognition by using wearable sensors, IEEE Trans. Inform. Technol. Biomed., vol. 16, no. 4, pp. 691–699, 2012.
[22]
J. Xie, C. Liu, Y. C. Liang, and J. Fang, Activity pattern aware spectrum sensing: A CNN-based deep learning approach, IEEE Commun. Lett., vol. 23, no. 6, pp. 1025–1028, 2019.
[23]
D. Cook, Learning setting-generalized activity models for smart spaces, IEEE Intell. Syst., vol. 27, no. 1, pp. 32–38, 2010.
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 10 May 2022
Revised: 10 August 2022
Accepted: 27 November 2022
Published: 19 May 2023
Issue date: October 2023

Copyright

© The author(s) 2023.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61872038).

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return