Sensors and Artificial Intelligence Methods and Algorithms for Human–Computer Intelligence Interaction

Abstract

The fusion of sensors and artificial intelligence (AI) methodologies has catalyzed a paradigm shift in human-computer interaction, providing a foundation for systems that can perceive, interpret, and respond to the world with unprecedented sophistication. This abstract provides a succinct overview of the interplay between sensors and AI methods, elucidating their pivotal role in shaping the landscape of human-computer intelligence interaction. Sensors serve as the frontline data collectors, translating real-world stimuli into digital information. From vision sensors capturing visual cues to environmental sensors monitoring ambient conditions, the diverse range of sensors acts as the sensory apparatus for intelligent systems. Simultaneously, AI methods, including machine learning and deep learning algorithms, empower these systems to extract actionable insights from the acquired data. Context-aware computing emerges as a hallmark of this integration, enabling systems to dynamically adapt their behavior based on environmental nuances. This adaptability enhances user experience by tailoring interactions to individual preferences and responding to changing contexts in real-time. The synergy between sensors and AI algorithms gives rise to adaptive, personalized, and responsive human-computer interactions. Applications in healthcare and robotics underscore the transformative impact of this synergy. Wearable sensors, coupled with AI-driven analytics, revolutionize health monitoring and diagnostics. In robotics, the integration of sensors and AI enables autonomous systems to navigate complex environments intelligently. These applications exemplify the potential for improving both individual well-being and the capabilities of intelligent machines.
Keywords: Human, Interaction, Sensors, Artificial, Intelligence, Health