Data-Driven Analysis of Patients Body Language in Healthcare: A Comprehensive Review

Body language refers to the unspoken communication conveyed through human body actions like body movements and postures, limb gestures, and facial and other bodily expressions. It acts as a transparent medium, exposing an individual's emotions, attitudes, true thoughts, intentions, and physical and mental health states. A person may express pain using hand movements or other bodily cues, their facial expressions potentially offering insights into the intensity of the pain. Additionally, various diseases and pains can induce abnormalities in body movements, postures, and expressions, signaling distress or discomfort. Therefore, investigating the cause-effect relationships between diseases/pains and patients' abnormal body language holds significant relevance, promising to enhance our understanding and management of these conditions. This importance has been reflected in numerous healthcare and artificial intelligence (AI) research articles. AI studies investigate this and related topics by detecting, recognizing, and analyzing patients' abnormal activities and body actions using machine-learning techniques. However, most AI studies do not consider comprehensive domain knowledge that describes a complete and accurate list of patients' abnormal actions caused by a disease or pain. Though these results appear consistent and stable from an AI outlook, they fall short when viewed through the prism of healthcare, primarily because the limited domain knowledge incorporated in the AI studies makes the findings partially incomplete. To overcome these drawbacks, this paper comprehensively reviews healthcare and medical studies centered on patients' body language from an AI outlook. It presents a thorough descriptive and exploratory analysis of the findings, yielding a more accurate and comprehensive understanding of the causational connections between diseases and abnormal body actions and the strength of the evidence supporting these connections. The analysis enables us to define "disease-to-abnormality" and "abnormality-to-disease" mappings that result in building exhaustive and accurate lists of abnormal body actions induced by diseases and pains as well as lists of diseases and pains causing particular abnormal body actions. The generation of these lists is assisted by the concepts of "correlation strength index" and "strongly correlated selection" defined in this paper. The paper's results have significant implications for developing machine learning systems that can more accurately analyze patients' physical and mental health states, correctly identify external signs and symptoms of diseases, and effectively monitor health conditions.