The first chapter of the book explores the transformative applications of
artificial intelligence (AI) in sensor technology and computer vision, focusing on
human activity recognition, health monitoring, medical imaging, and autonomous
vehicles within the automotive industry. It highlights the substantial advancements AI
brings to these fields, particularly emphasizing the roles of machine learning (ML) and
deep learning (DL), a subset of ML. In the field of human activity recognition and
health monitoring, AI's ability to enhance accuracy and efficiency is thoroughly
examined. The discussion extends to medical imaging, where ML and DL techniques
significantly improve diagnostic processes and patient outcomes. The chapter also
delves into the automotive industry, showcasing AI's impact on enabling self-driving
cars and optimizing manufacturing processes. Each section provides detailed insights
into the potential capabilities of ML and DL, illustrating AI's role as a game-changer
that revolutionizes traditional methods. The narrative underscores the transformative
power of these technologies, driving innovation and creating new opportunities across
various domains. Additionally, the chapter addresses the challenges faced in the
construction and operation of ML models. It analyzes difficulties such as data quality
issues, computational resource demands, and algorithmic training complexities,
offering a balanced perspective on the promises and hurdles of AI deployment. The
chapter concludes with an in-depth discussion on sensor data collection and processing
and case studies to demonstrate AI applications in real life. This section covers
methodologies for gathering high-quality sensor data, pre-processing techniques, and
integrating this data into AI frameworks, setting the stage for understanding AI's
profound impact and technical intricacies.
Keywords: AI, Computer vision, Machine learning, Sensors.