Binary cuckoo search metaheuristic-based supercomputing framework for human behavior analysis in smart home

Human activity recognition has been a topic of attraction among researchers and developers because of its enormous usage in widespread region of human life. The varied human activities and the way they are executed at individual level are the main challenges to be recognized in human behavior modeling. This paper proposes a novel methodology that recognizes human activities from the behavior of individuals in a smart home environment. The dataset considered in this work is captured using Bluetooth low energy, a popular technology for indoor localization. The proposed framework is a binary cuckoo search-based stacking model that collectively exploits multiple base learners for human activities recognition from the gathered accelerometer sensors data mounted on wearable and mobile devices. The work is tested on the newly developed SPHERE dataset to recognize user activities in smart home environment. The experimental results confirm the effectiveness of the proposed approach, which outperforms MLP, DT, KNN, SGD, NB, RF, LR and SVM classifiers on the dataset and gives a high predictive accuracy value of 93.77% via a tenfold cross-validation. The proposed approach gives a better performance at the expense of more computation time, that is, due to the integration of cuckoo search metaheuristic algorithm.