Wearable Sensing and Vision Technologies for Human Activity Recognition in Daily Life

Dear Colleagues,

Human activity recognition plays a key role in the development of intelligent technologies applied to connected health, rehabilitation, sports monitoring, assistive systems, and domestic robotics. The emergence of wearable sensors and embedded vision systems enables continuous and non-intrusive analysis of human behavior in real-world conditions. However, activity variability, sensor noise, energy constraints, context changes, and privacy concerns pose major challenges to achieving robust and reliable recognition.

This Special Issue aims to gather contributions from academia and industry showcasing recent advances in wearable sensing, computer vision, multimodal fusion, and deep learning for human activity recognition in daily life. It will offer a platform for innovative research, systematic reviews, and application-oriented studies addressing real-world recognition, privacy preservation, personalization, interpretability, and anomaly detection. The objective is to foster the development of efficient, explainable, and adaptable systems suitable for diverse everyday scenarios.

Dr. Benjamin Allaert
Prof. Dr. Marius Bilasco
Dr. Andon Tchechmedjiev
Guest Editors

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