Improving Task-Incremental Human Activity Recognition with Plasticity Techniques - S., F. G. Brabes da; Lima, W. G. S.; S., A. Anjos da; Colombini, E. L.; Ghedini, C. G. (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2024: Anais do XII Symposium on Knowledge Discovery, Mining and LearningDO - 10.5753/kdmile.2024.244696, 2024)
Sensor-based Human Activity Recognition (HAR) has been applied across various domains, including healthcare monitoring, fitness tracking, and smart home systems. These applications require the ability to accurately detect and respond to a wide range of human activities, each with varying distributions, which imposes a significant challenge. The task-incremental learning paradigm can address this problem by enabling HAR systems to adapt to changes in distribution and learn new activities over time without forgetting the previously learned ones. Continual adaptation is essential for maintaining high performance, as it allows the system to effectively respond to these changes. Although several strategies in the continual learning literature have been evaluated for task-incremental scenarios in HAR, there is still room for improvement, as the results are not as good as those achieved with conventional approaches. This work proposes two new neuroplasticity-inspired techniques that can be integrated with any learning strategy. Inspired by the brain’s ability to reorganize and strengthen connections over time, these methods focus on enhancing the model’s flexibility and long-term knowledge retention. The proposed techniques were evaluated alongside the WA-ADB and WA-MDF strategies on well-known HAR datasets. Experimental results demonstrated that the new techniques significantly enhanced the models’ ability to retain knowledge, which holds significant potential for improving the robustness and longevity of HAR systems in real-world applications.