Human activity recognition (HAR) is growing in popularity due to its wide-ranging
applications in patient rehabilitation and movement disorders. HAR approaches typically …

The rise of intelligent systems and smart spaces has opened up new opportunities for
human–machine collaborations. Interactive Machine Learning (IML) contribute to fostering …

Human activity recognition (HAR) has increased in recent years due to its applications in
mobile health monitoring, activity recognition, and patient rehabilitation. The typical …

Abstract Human Activity Recognition (HAR) has been attracting research attention because
of its importance in applications such as health monitoring, assisted living, and active living …

Human-AI collaboration has evolved into a complex, multidimensional paradigm shaped by
research in various domains. Key areas such as human-in-the-loop systems, Interactive …

Empowered by machine learning, edge devices including smartphones, wearable, and IoT
devices have become growingly intelligent, raising conflicts with the limited resource. On …

Abstract Calibrating Human Activity Recognition (HAR) models to end-users with Transfer
Learning (TL) often yields significant accuracy improvements. Such TL is by design done …

Human action recognition models often suffer from achieving both accurate recognition and
subject independence when the amount of training data is limited. In this paper, we propose …

Edge Computing (EC) has gained significant traction in recent years, promising enhanced
efficiency by integrating Artificial Intelligence (AI) capabilities at the edge. While the focus …

Training only the last few layers in deep neural networks has been considered an effective
strategy for enhancing the efficiency of on-device training. Prior work has adopted this …