Machine Learning for Human Activity Recognition: State-of-the-Art Techniques and Emerging Trends.

ABSTRACT: Human activity recognition (HAR) has emerged as a transformative field with widespread applications, leveraging diverse sensor modalities to accurately identify and classify human activities. This paper provides a comprehensive review of HAR techniques, focusing on the integration of sensor-based, vision-based, and hybrid methodologies. It explores the strengths and limitations of commonly used modalities, such as RGB images/videos, depth sensors, motion capture systems, wearable devices, and emerging technologies like radar and Wi-Fi channel state information. The review also discusses traditional machine learning approaches, including supervised and unsupervised learning, alongside cutting-edge advancements in deep learning, such as convolutional and recurrent neural networks, attention

SUBMITTER: Hossen MA 

PROVIDER: S-EPMC11943402 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature