Rio Darma Wanto

Whole-body vibration (WBV) is a critical factor affecting driver comfort, fatigue, occupational health, and road safety. This chapter presents an AI-based monitoring framework that integrates vibration sensors, signal processing, machine learning, risk classification, and mitigation responses to assess driver exposure in real time. The framework transforms raw vibration data from seats, steering wheels, floors, wearables, smartphones, and vehicle systems into meaningful indicators for fatigue detection, comfort assessment, and safety evaluation. It also emphasizes sensor placement, data quality, AI reliability, standards compliance, and human-centered intervention. By supporting alerts, rest-break recommendations, smart seat adjustment, adaptive damping, and fleet-level decision-making, the proposed approach promotes proactive monitoring to reduce fatigue, improve comfort, protect occupational health, and enhance safer transportation systems in modern mobility environments.

The advancement of artificial intelligence has revolutionized the modern sports ecosystem by transforming performance analysis, tactical decision-making, and fan engagement. AI enables real-time monitoring of athletes' physiological conditions through wearable devices and processes the data using edge computing to ensure fast and secure responses. In game strategy, AI is used to analyze team formations, predict match scenarios, and support evidence-based decisions, replacing intuition with data. In the commercial domain, technologies such as NLP and Generative AI allow content personalization, automated highlight generation, and enhanced experiences in smart stadiums. However, the integration of AI also brings ethical challenges, including athlete data privacy, algorithmic bias, and technological inequality. Therefore, the success of AI implementation in sports depends heavily on ethical governance, multidisciplinary collaboration, and an inclusive, adaptive approach to innovation.

This chapter examines the convergence of quantum computing and artificial intelligence through the emerging field of Quantum Artificial Intelligence (QAI). The discussion explains how quantum principles such as superposition, entanglement, and quantum interference can enhance machine learning, generative AI, optimization, and intelligent systems beyond the limitations of classical computing. The chapter presents concepts of quantum computing, quantum machine learning, quantum neural networks, and hybrid quantum-classical architectures while also discussing applications in healthcare, cybersecurity, finance, manufacturing, and scientific research. In addition, the chapter addresses challenges related to scalability, explainability, cybersecurity, governance, ethics, and unequal access to quantum technologies. The chapter also introduces a conceptual QAI Ecosystem Model and outlines future research directions involving Quantum-enhanced Explainable AI, agentic AI, quantum internet systems, and quantum-safe cybersecurity.

The advancement of artificial intelligence has revolutionized the modern sports ecosystem by transforming performance analysis, tactical decision-making, and fan engagement. AI enables real-time monitoring of athletes' physiological conditions through wearable devices and processes the data using edge computing to ensure fast and secure responses. In game strategy, AI is used to analyze team formations, predict match scenarios, and support evidence-based decisions, replacing intuition with data. In the commercial domain, technologies such as NLP and Generative AI allow content personalization, automated highlight generation, and enhanced experiences in smart stadiums. However, the integration of AI also brings ethical challenges, including athlete data privacy, algorithmic bias, and technological inequality. Therefore, the success of AI implementation in sports depends heavily on ethical governance, multidisciplinary collaboration, and an inclusive, adaptive approach to innovation.