Résumé
The recent reform of medical education in France, including the introduction of Objective Structured Clinical Examinations (OSCEs), underscores the need to standardize the learning and evaluation of clinical skills. Among these, palpation—a core component of the physical examination—relies on haptic perception, where practitioners assess the biomechanical properties of tissues through tactile and proprioceptive cues. Yet, palpation remains largely taught through traditional apprenticeship models, lacking standardized protocols or objective pedagogical tools. Modernizing the learning of touch in healthcare therefore represents a major educational challenge. The HAPTIMED project investigates the sense of touch as an active movement that structures diagnostic and therapeutic interaction. Anchored at the intersection of human movement sciences, electronics, and artificial intelligence, it aims to model and enhance the acquisition of haptic competence in healthcare education through the development of a digital twin of haptic perception. The project has just begun and brings together two mirror doctoral theses within a unified interdisciplinary framework: Topic A (Human Movement Sciences) focuses on the analysis and standardization of palpatory gestures. It seeks to identify objective markers of haptic expertise and to define pedagogical feedback mechanisms for learners. The study will characterize expert and novice gestures in terms of force, displacement, and temporal dynamics, providing a reference framework for training and assessment. Topic B (Flexible Electronics, Structures and Embedded Systems) aims to design and validate a wearable haptic glove capable of monitoring pressure and movement during palpatory examinations. Based on Hooke’s law, tissue stiffness (k), the ratio between applied force and resulting deformation, is treated as the key measurable parameter linking force and displacement. By quantifying stiffness, the glove will provide an objective measure of tissue resistance to deformation, allowing learners to visualize and compare their applied pressure with expert benchmarks. A transversal methodological axis combines machine learning and belief function theory to fuse multimodal data (force, displacement, motion trajectories) and to manage uncertainty in haptic signal interpretation. Ultimately, HAPTIMED aims to modernize and objectify the teaching of clinical touch, providing evidence-based recommendations for OSCE implementation. By rendering touch measurable, comparable, and computable, the project exemplifies how movement computing can bridge human expertise and digital intelligence—transforming the embodied act of palpation into a quantifiable and pedagogically actionable dimension of healthcare education.