Application of Machine Learning in Human Activity Recognition
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".
Deadline for manuscript submissions: 31 July 2025 | Viewed by 18
Special Issue Editors
Interests: pervasive computing; human–computer-interaction; IoT
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid advancement of machine learning (ML) and large language models (LLMs) has significantly impacted numerous domains, including human activity recognition (HAR). As these techniques become more sophisticated and accessible, their application in HAR opens up new opportunities for enhancing industries such as healthcare, sports, security, and smart environments. This Special Issue on “Application of Machine Learning in Human Activity Recognition” aims to explore innovative approaches, methodologies, and applications of ML and LLMs in recognizing and interpreting human activities accurately and efficiently.
Human activity recognition involves the automatic detection and classification of physical activities using data from various sensors. With the advent of wearable technology, smartphones, and IoT devices, the availability of rich datasets has grown, necessitating advanced ML and LLM models to analyze and interpret these data effectively. This Special Issue seeks to highlight cutting-edge research that demonstrates how ML and LLMs can be leveraged to improve the accuracy, reliability, and applicability of HAR systems in real-world scenarios. We look forward to receiving your submissions and contributing to this exciting and rapidly evolving field.
Prof. Dr. Yang Gao
Guest Editor
Dr. Shibo Zhang
Co-Guest Editor
Manuscript Submission Information
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Keywords
- deep learning techniques and architectures for HAR
- sensor fusion and data preprocessing methods for activity recognition
- real-time activity recognition systems and their applications
- transfer learning and domain adaptation in HAR
- privacy-preserving ML methods in activity recognition
- multimodal data integration for robust activity recognition
- HAR in healthcare (elderly care, rehabilitation), sports analytics and performance monitoring, security, and smart environments
- challenges and solutions in large-scale deployment of har systems
- case studies and real-world implementations of ML-based HAR systems
- large language models (LLMs) for interpreting and enhancing HAR data
- integration of llms for real-time decision-making and activity prediction
