Body Representation in Stroke Patients: A Systematic Review of Human Figure Graphic Representation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".