Precision-Oriented Sensing and Multimodal Visuo-Haptic Interaction for Fine Manipulation in Biomedical Engineering and Assistive Applications

Virtual Reality (VR) simulations in high-stake domains, such as surgical training or robotic teleoperation, require both high visual fidelity and accurate force feedback to facilitate embodied cognition and prevent errors. However, current commercial haptic devices impose a critical trade-off: while they provide force feedback, their embedded sensors are known to be prone to latency, hysteresis, and tracking noise, which may limit the precise finger flexion tracking necessary for fine manual dexterity. Conversely, data gloves designed specifically for precise finger tracking offer finer input granularity but lack active force feedback capabilities. To address these limitations, we introduce a hybrid architecture that simultaneously deploys precision-oriented data gloves and haptic gloves, presenting a proof-of-concept user study to examine the performance benefits and trade-offs of combining precision-oriented tracking with direct haptic feedback during the fine manipulation of delicate virtual objects. From our findings, we derive insights into optimizing multimodal visuo-haptic interaction for future biomedical and assistive environments.