A COMPREHENSIVE EXAMINATION OF PATIENT BODY LANGUAGE USING DATA-DRIVEN METHODS

Abstract

Patient body language plays a critical role in clinical communication, influencing diagnosis accuracy, therapeutic relationships, and overall healthcare outcomes. This review presents a comprehensive examination of how data-driven methods are used to analyze and interpret patient body language within healthcare environments. The study explores a range of computational techniques, including machine learning, deep learning, computer vision, and sensor-based analytics, that enable the automated detection of nonverbal cues such as posture, facial expressions, gestures, and movement patterns. Current advancements, challenges, and applications are synthesized to highlight how these technologies support early diagnosis, patient monitoring, mental-health assessment, and enhanced clinician–patient interaction. Additionally, limitations related to data availability, privacy, model generalization, and ethical concerns are discussed.