Abstract
In this research, we look at the notion of spatio-temporal and create context information from the smart home environment for multiple resident activity recognition analysis. We created context features from the smart home environment in order to correlate with the ambient sensor pattern from activities of daily life (ADL) of the multiple residents that reside in the same environment. An Adaptive Profiling model was generated based on the possible interaction activity using Expectation-Maximization (EM) clustering. The clusters parameter is adaptively generated from the active label sets from the real-world datasets at the processing stage. The proposed Adaptive Profiling model was classified using multi-label classification using the label combination approach and compared with different classifiers such as Random Forest, Support Vector Machine, and k-NN method. The model with Adaptive Profiling achieved higher accuracy when it was compared with only sensor triggering patterns. The results obtained also were compared with the previous works to justify its performance.
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Mohamed, R., Zainudin, M.N.S., Perumal, T., Muhammad, S. (2022). Adaptive Profiling Model for Multiple Residents Activity Recognition Analysis Using Spatio-temporal Information in Smart Home. In: Alfred, R., Lim, Y. (eds) Proceedings of the 8th International Conference on Computational Science and Technology. Lecture Notes in Electrical Engineering, vol 835. Springer, Singapore. https://doi.org/10.1007/978-981-16-8515-6_60
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DOI: https://doi.org/10.1007/978-981-16-8515-6_60
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