Transfer learning for estimating occupancy and recognizing activities in smart buildings

Activities Recognition (AR) and Occupancy Estimation (OE) are vital to many smart systems that work on providing good services in smart buildings . Many applications, such as energy management need information like activities and occupancy to provide good assistance. Most of the previous research about AR and OE focused on applying supervised machine learning methods . Researchers train a model and evaluate it using data collected from the same environment (Domain). A model trained in a specific domain will not generalize well in other domains. Creating a trained model to every environment is not feasible due to the lack of data. Collecting sufficient data can be time consuming and infeasible in some cases. Computational power can be a challenge for researchers by increasing the training time due to the lack of the required computing resources. Using traditional machine learning methods, the obtained performance may be unsatisfactory, and can not lead to optimal solutions. For all these reasons, we need a solution that helps us overcome the stated problem and obtain models with acceptable results. In this work, we present and discuss different transfer learning methods that help us transfer knowledge from a source domain to a target domain. The goal is to reuse as much as possible information from the source domain to enhance the performance of the model at the target domain. This type of approaches will solve the problems mentioned before such as the lack of data and will provide us with good results due to the use of knowledge from multiple source domains. We tested five Transfer learning (TL) approaches: a principal component analysis (PCA)-like method that creates a transformation like the PCA transformation and apply it to the data to create new common domain, a PCA based method that creates common domain using PCA, a PCA-SMOTE method that balances the data and creates common domain, a basic method based on a simple matching between similar features from source and target domain, and a sparse coding-based method that creates a common domain where the data representation will be as sparse as possible. The impressive results that we obtained in both tasks prove that the presented methods can be applied to transfer knowledge across different domains.

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