Foreword

The book titled " Artificial Intelligence Development in Sensors and Computer Vision for Health Care and Automation Application" is an essential resource for anyone who wants a thorough understanding of the significant impact of artificial intelligence (AI) in electronics, specifically in sensor technology, computer vision, and machine learning. It provides comprehensive insights into the transformative role of AI in these areas, making it a valuable asset in the rapidly evolving area of AI. I wholeheartedly recommend this book for its insightful exploration of cutting-edge technologies and their applications.

In this well-organized research, Dr. Minh Long Hoang successfully leads readers through an illuminating exploration that encompasses subjects ranging from inertial measurement unit (IMU) sensors to light detection and ranging (lidar) and radio detection and ranging (radar). Through the lens of machine learning models, the author demonstrates how IMU data can be utilized for diverse purposes, such as process optimization, risk prevention, fault diagnosis, and human activity recognition. The integration of lidar and radar sensors into self-driving cars and AI robotic systems adds an extra layer of depth to the discussion, providing real-world examples of how these technologies are reshaping our future.

Moreover, the exploration of computer vision is equally captivating, focusing on image recognition, motion tracking, and object classification. The book also introduces robust AI algorithms like convolutional neural networks (CNN) and you only look once (YOLO), showcasing their applications in healthcare and automated vehicle control. Additionally, the book sheds light on the role of deep learning in human pose estimation (HPE) for rehabilitation support and also examines the uncertainty of deep neural network (DNN) predictions, particularly in IMU data.

The concluding chapter seamlessly ties together the comprehension gained from the earlier discussions, exploring the incorporation of machine learning into augmented reality (AR) within the automotive industry. It highlights the significant potential of AI in enhancing the design process, manufacturing, and customer experience in the automotive sector.

Overall, this book is highly recommended for professionals, researchers, and students seeking a comprehensive and up-to-date knowledge of the symbiotic relationship between AI, sensors, and computer vision. The book not only demystifies complex concepts but also inspires readers to explore the limitless possibilities that arise at the intersection of these transformative technologies.