This book provides an in-depth analysis of the design and implementation of a smartphone sensor-based Human Activity Recognition (HAR) system using advanced learning models. It offers detailed insights into key advancements and future directions for HAR system development. The content includes a comprehensive literature review on wearable sensor-based HAR, along with descriptions of publicly available datasets for building HAR systems. To overcome the limitations of existing sensor-based HAR systems, the book emphasizes the creation of a robust HAR dataset in uncontrolled environments. This dataset is developed using built-in smartphone sensors, enabling real-time activity classification. The book presents multiple effective and optimized classifiers for developing efficient HAR systems, leveraging both self-generated and publicly available data.

The book demonstrates high effectiveness in real-time activity classification and addresses common challenges such as class imbalance and domain shift. It provides practical methodologies and techniques to enhance HAR system performance. This book is an essential resource for professionals, healthcare practitioners, academics, researchers, and students specializing in health applications, signal processing, machine learning, ensemble learning, and deep learning. It offers extensive analysis and actionable insights for investigators, helping streamline product development through intelligent learning algorithms in the field of Human Activity Recognition.

Discusses in-depth design issues of activity recognition chain in Human Activity Recognition system Offers future trends and challenges for potential human activity recognition system development Integrates data collection and activity recognition modules using advanced learning algorithms for effective development

Autorentext

Nurul Amin Choudhury is a researcher in the field of artificial intelligence and wearable sensors. He holds a Ph.D. in Computer Science and Engineering from the National Institute of Technology (NIT) Silchar, where he previously served as a Senior Research Fellow and Junior Research Fellow in the Multimedia and Image Processing Lab under the Department of Computer Science and Engineering. He earned his M.Tech and B.Tech degrees from the National Institute of Technology, Meghalaya, and Jawaharlal Nehru Technological University, Hyderabad, in 2021 and 2018, respectively. His research interests include artificial intelligence, machine learning, IoT, AI-IoT in healthcare, human-computer interaction, feature engineering, and sensor-based human activity recognition, with a strong focus on real-world, uncontrolled environments and dataset development. Badal Soni is an Assistant Professor in the Department of Computer Engineering at the National Institute of Technology (NIT) Silchar, India. He completed his B.Tech at Rajiv Gandhi Technical University (formerly RGPV), Bhopal, and his M.Tech at the Indian Institute of Information Technology Design and Manufacturing (IIITDM), Jabalpur. Dr. Soni earned his Ph.D. from NIT Silchar. With over twelve years of teaching and research experience in computer science and information technology, he specializes in image processing, machine learning, and natural language processing. Dr. Soni has published over 95 papers in international journals and conference proceedings, authored two books, and edited five books. He is a senior member of IEEE and holds professional memberships in several organizations, including IEEE, ACM, IAENG, and IACSIT.

Inhalt

Introduction.- Literature Survey.- Design and development of a comprehensive smartphone sensor based HAR Dataset in an uncontrolled environment for efficient HAR.- Indepth Analysis of Design and Development for Sensor based HAR System.- Data Intensity based Feature Selection Protocol for Sensor-based HAR System.