This paper systematically reviews intelligent tutoring systems (ITS) that utilize gross body movement detection through computer vision. By analysing recent advancements, methodologies, and applications, we identify key trends and challenges in leveraging movement data to enhance personalized learning experiences. The study highlights how computer vision techniques enable real-time monitoring and adaptive feedback, promoting active and engaging educational environments. The integration of artificial intelligence and computer vision in healthcare has revolutionized diagnostics, patient monitoring, and surgical assistance. This review explores state-of-the-art techniques and applications, emphasizing accuracy, real-time processing, and ethical considerations. Key challenges such as data privacy and interpretability are discussed, along with prospects for enhancing healthcare outcomes through AI-driven vision systems. This comprehensive review traces the evolution of computer vision and pattern recognition techniques used for left ventricle segmentation over the past five decades. By examining methodological progress and clinical applications, the study highlights major breakthroughs and ongoing challenges, paving the way for improved cardiac imaging and diagnostic accuracy. Sensor planning plays a pivotal role in optimizing computer vision systems by strategically positioning sensors for maximal data acquisition. This survey covers fundamental concepts, recent advancements, and practical implementations, offering insights into multi-sensor coordination and adaptive strategies for enhanced visual perception. The construction industry is increasingly adopting computer vision technologies to enhance safety by monitoring hazardous zones, detecting safety violations, and analysing worker movements. This paper explores the latest applications and how AI-driven systems are reducing workplace accidents and improving compliance with safety protocols
Subham et al. (Sat,) studied this question.