Abstract

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.