Entropy and Memory Aware Active Transfer Learning in Smart Sensing Systems

Automated Human Activity Recognition (HAR) stems from the requirement to seamlessly integrate technology into daily life, to enhance user experience, improve healthcare, provide improved operations, ensure safety, deliver data-driven insights, and address various real-world challenges. However, unscripted Human activity faces challenges that must be understood, and require advances in sensor technology and machine learning models. This paper presents an Active Transfer Learning (ATL) approach for real-time HAR using mobile sensor data. Unlike traditional methods, our approach accounts for both the physical and habitual constraints of individuals. Existing works make an unrealistic assumption of an omniscient oracle while using the same datasets for both training and testing of the models, which makes them impractical for industry requirements. Our proposed approach addresses challenges in existing HAR algorithms, proposing a methodology to adapt models to the real-world datasets while training and testing on cross datasets. We have tailored an existing Entropy and Memory Maximization algorithm to work in a real-time environment while considering user constraints. Primarily trained in a well-labeled controlled environment dataset, we introduce noise injection to prevent the model from overfitting and enhance its generalization for scarcely labeled real-world datasets. Evaluations on publicly available datasets demonstrate our approach achieves 80% - 90% accuracy, outperforming the base algorithm accuracy of 12% - 14%. Importantly, our proposed technique outperforms with limited labeled data, making it valuable for real-time scenarios where labeling is sparse. This research advances HAR in real-world settings, offering improved accuracy and adaptability.