Progress in Systems and Telecommunication Engineering

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Progress in Systems and Telecommunication Engineering

Hierarchical Multi-label Classification for Activity Recognition

Nina ReÃ…Â¡cic, Mitja LuÃ…Â¡trek
Jozef Stefan Institute International Postgraduate School Jozef Stefan Jamova cesta 39 1000 Ljubljana

Abstract: researched. Most commonly classification considers all activities to be Ã¢â‚¬ËœequalÃ¢â‚¬â„¢ (we will use term at classification). However, intuition suggests better results could be achieved using a hierarchical approach for classification. In this paper we compare three different approaches to classify activities: (i) Flat classification - classes are equal and we build one model to classify all of them; (ii) Multi-model hierarchical classification - classes are arranged in trees, we build different models to classify activities on different levels. We apply two different approaches; (iii) Hierarchical classification using CLUS software1.

Keywords: Activity Recognition, Hierarchical Multi-label Classification, Wearable Sensors Hierarchical Multi-label Classification for Activity Recognition

DOI:

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References:[1] Attal, F., Mohammed, S., Dedabrishvili, M., Chamroukhi. F., Oukhellou, L., Amirat, Y. (2015). Physical Human Activity Recognition Using Wearable Sensors. Sensors 15(12). Basel 31314-18. [2] Siirtola, P., Lurinen, P., Haapalainen, E., Rnoning, J., Kinnunen, H. (2009). Clustering-based activity classification with a wristworn accelerometer using basic features. In: 2009 IEEE Symposium on Computational Intelligence and Data Mining. CIMD 2009 - Proceedings. 95-100. [3] Chernbumroong, S., Atkins, A.S. (2011)Activity classification using a single wrist-worn accelerometer. In: 2011 5th International Conference on Software, Knowledge Information, Industrial Managrment and Applications (SKIMA) Proceedings. 1-6. [4] Cvetkovic B., Drobnic V., Lustrek M. (2017) Recognizing Hand-Specic Activities with a Smartwatch Placed on Dominant or Non-dominant Wrist. In: Information Society. Ljubljana [5] Nweke, H.F., Teh, Y.M., Al-gardi, M.A., Alo, U.R.(2018). Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challanges. Expert Systems With Applications. 105. 233-261.
[6] Vens, C., Stryuf, J., Schietgat, L., Dzeroski, S., Blockeel H. Decision trees for hierarchical multi-label classsification. In: Machine Learning, 73 (2) 185-214.
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[8] Khan, A.M., Lee, Y.K., Lee, S.Y., Kim, T.S. (2010). A Triaxial Accelerometer-Based Physical-Activity recognition via Augmented- Signal Features and a Hierarchical Recognizer. In: IEEE Transactions on Information Technology in Biomedicine. 14 (5) 1166-72
[9] Zheng, Y. (2015). Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework. Journal of Electrical and Computer engineering. doi:10.1155/2015/140820
[10] Paes, B.C., Plastino, A., Freitas, A.A. (2012). Improving Local Per Level Hierarchical Classification. Journal of Information and Data Manegement 3 (3) 394-409.
[11] Paes, B.C., Plastino, A., freita s, A.A. (2014). Exploring Attribute Selection in Hierarchical Classification. In: Journal of information and Data manegment- Vol. 5 (1) 124-133.
[12] Blockeel, H., Bruynooghe, M., DÃ¢â‚¬â€zeroski, S., Ramon, J., Struyf; J. (2002). Hierarchical mulit-classification. In: Proceedings of the ACM SIGKDD 2002 Workshop on Mulit-Relational Data Mining (MRDM 2002). 21-35.
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[14] Clus Homepage (last accessed 26 Jun 2018) https://dtai.cs.kuleuven.be/clus/index.html.

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