Teaching oral care via vision-based deformation perception

This paper presents a novel, cost-effective sensor platform based on Vision-based Deformation Perception (VBDeformP) for community oral health education. The system integrates a 3D-printed thermoplastic polyurethane soft structure with a rigid resin frame and an ArUco marker to encode interaction information, including the contact region and six-dimensional force and torque. By transforming force estimation into a marker-based pose tracking problem, the VBDeformP sensor achieves accurate and robust force/torque inference under both quasi-static and dynamic conditions, utilizing machine learning models. An adaptive image binarization algorithm extends reliable marker detection across a wide illumination range (10-5,000 lx), ensuring consistent performance of the vision system in realistic community teaching scenarios. Experimental validation involving 10 healthy participants per-forming standardized brushing tasks demonstrated that the sensor attains measurement accuracies comparable to a commercial ATI Axia80-M20 sensor, with mean absolute errors of 0.55 N (2.19% relative error) and 0.067 N·m (2.68% relative error) for quasi-static forces and torques, and 0.16 N (4.10% relative error) and 0.023 N·m (5.75% relative error) under dynamic conditions. Moreover, the system’s real-time brushing region classification algorithm achieved an overall accuracy of 98.12%, further underscoring its potential to provide immediate and personalized guidance on oral hygiene. Its low cost, rapid initialization, portability, and scalable fabrication render it a promising solution for enhancing oral health education in community settings.