Body Representation in Stroke Patients: A Systematic Review of Human Figure Graphic Representation

Background: Body representation is a complex process involving sensory, motor, and cognitive information. Frequently, it is disrupted after a stroke, impairing rehabilitation, emotional functioning, and daily functioning. The human figure graphic representation has emerged as a holistic tool to assess post-stroke outcomes. Objectives: This systematic review examines the methodologies of human figure representation tests and their application in assessing post-stroke body representation, emphasizing its role in bridging subjective patient experiences with objective metrics. Methods: This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. A literature search was conducted through the databases PubMed, Scopus, Embase, Web of Science, and Google Scholar, collecting publications eligible for qualitative analysis. We selected studies where patients drew human figures in the study design to assess body representation, involving exclusively the adult stroke population. The Newcastle–Ottawa Scale was used to assess the risk of bias. Results: Ten studies were analyzed. The tool demonstrated versatility in capturing unilateral spatial neglect, emotional disturbances, and functional independence. Qualitative metrics and quantitative indices correlated with cognitive deficits, mood disorders, and activities of daily living. Human figure representation also predicted rehabilitation outcomes, with improvements aligning with motor recovery. Innovations included digital quantification of evaluation metrics. Conclusions: Human figure graphic representation is a low-cost, adaptable tool bridging motor, cognitive, and emotional assessments in stroke survivors. While methodological variability persists, AI-driven analytics and standardized frameworks could enhance objectivity. Future research should prioritize validating parameters and developing hybrid models combining traditional qualitative insights with machine learning, thus advancing precision neurorehabilitation and personalized care.