Skill Assessment

Skill assessment research focuses on objectively and automatically evaluating proficiency in various tasks, ranging from sports to complex surgical procedures, aiming to replace subjective human evaluation with reliable, data-driven methods. Current research employs diverse approaches, including deep learning models (e.g., convolutional and transformer networks) analyzing video data and motion capture to extract relevant features and predict skill levels, often incorporating techniques like self-supervised learning and meta-learning to address data scarcity and improve generalizability. These advancements offer significant potential for improving training, certification, and performance feedback across numerous fields, leading to enhanced efficiency and safety in areas like healthcare and sports. The development of more nuanced and robust assessment methods is a key focus, addressing limitations in existing techniques and biases in established metrics.