Unsupervised domain adaptation without source data for estimating occupancy and recognizing activities in smart buildings

Activities Recognition (AR) and Occupancy Estimation (OE) are topics of current interest. AR and OE help many smart building applications such as energy systems and provide good services for occupants. Prior research on AR and OE has typically focused on supervised machine learning methods. For a specific smart building domain, a model is trained using data collected from the current environment (domain). The created model will not generalize well when evaluated in a new related domain due to data distribution differences. Creating a model for each smart building environment is infeasible due to the lack of labeled data. Indeed, data collection is a tedious and time-consuming task. Unsupervised Domain Adaptation (UDA) is a good solution for the considered case. UDA solves the problem of the lack of labeled data in the target domain by allowing knowledge transfer across domains. Most of the previous research in UDA requires having access to source data while creating target models which leads to privacy problems. This work considers techniques that use only a trained source model instead of a huge amount of source data to make domain adaption. This research adapted and tested UDA methods called Source HypOthesis Transfer (SHOT), Higher-Order Moment Matching (HoMM), and Source data Free Domain Adaptation (SFDA) on smart building data. SHOT is a deep learning method that learns a feature encoding module for the target model to align the data representation of the target environment with the data representation of the source environment, and it freezes the hypothesis (classifier) of the source model. Data alignment is done using information maximization and self-supervised pseudo-labeling. HoMM is also a deep learning method, however, it freezes the feature encoding module, and it learns a classifier to perform data alignment. HoMM also performs pseudo-labeling to target samples to enhance data alignment. SFDA is a deep domain adaptation method that optimizes two losses to train the target model without using any source-labeled data. The target model updates its initialized weights from the source model by minimizing a first loss that uses pseudo labels of target samples using the pre-trained source model, and by minimizing a second loss that uses pseudo labels of target samples generated by the trainable target model. To prove the efficiency of SHOT, HoMM, and SFDA, this research tests them on AR and OE datasets for a different number of activities and levels of occupancy. The impressive obtained results, with scores up to 90% for OE and up to 97% for AR, show that the considered approaches can be used to transfer knowledge across different related domains.