Abstract
As the population ages and health-care costs increase, smart environments can be an effective and economical way to provide care and support for the aged population. Human activity recognition (HAR), a key element of the smart environment research domain, has garnered a lot of attention lately. The present work is to provide a data-driven solution based on the extended belief rule base (EBRB) model for sensor-based HAR in the context of big data. More specifically, in order to increase the efficiency of the EBRB model, this research first offers a new rule generation method based on probability estimation, which forms the link between the extended belief rules and human activities. The number of extended belief rules used to extract knowledge from a sensor-based HAR dataset is exactly equal to the types of human activities, and each rule can be thought of as a collection of class conditional probability distributions. As a result, it is possible to create an EBRB-BD model, an EBRB model for HAR using big data that has a compact but representative rule base. The effectiveness of the EBRB-BD model is further supported by case studies. Experimental findings demonstrate that the modelling time of the EBRB-BD model is one in ten-thousand of the original EBRB model, and the EBRB-BD model also achieves the best area under the curve value (AUC) of 94.95\(\%\), surpassing the original EBRB model and some other benchmark classifiers.
Supported by the National Natural Science Foundation of China (No. 72001043), the Natural Science Foundation of Fujian Province of China (Nos. 2022J01178 and 2020J05122), and the Humanities and Social Science Foundation of the Ministry of Education of China (No. 20YJC630188).
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Ren, TY., Yang, LH., Nugent, C., Ye, FF., Irvine, N., Liu, J. (2023). Extended Belief Rule Base Model with Novel Rule Generation for Sensor-Based Human Activity Recognition Under Big Data. In: Bravo, J., Ochoa, S., Favela, J. (eds) Proceedings of the International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2022). UCAmI 2022. Lecture Notes in Networks and Systems, vol 594. Springer, Cham. https://doi.org/10.1007/978-3-031-21333-5_73
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