Improving Transfer Learning and Domain Adaptation in Smart Homes: The Role of Dynamic Domain Adaptation Layer

The advent of smart home technologies has opened new avenues for personalized healthcare, energy management, and enhanced convenience. However, variations in resident behavior, sensor types, and home layouts cause domain shifts, posing significant challenges to the deployment of machine learning models across diverse smart home environments. This paper describes a new method that uses domain adaptation layers in a transfer learning framework to change domain-specific features on the fly. This makes cross-data set generalization better. We compare the proposed method against Standard Transfer Learning, Domain-Adversarial Neural Networks (DANN), and Correlation Alignment (CORAL) using multiple smart home data sets. Our evaluation demonstrates that the proposed method significantly outperforms these existing approaches in terms of predictive performance, highlighting its effectiveness in addressing domain shifts in smart home environments.