Personalized Soft Robotic Exoglove Uses 3D Scans for Precision Hand Rehab

A new academic paper from Paul Dela Cruz, Mostafa Mo. Massoud, and Jacqueline Libby introduces a personalized soft robotic exoglove designed to improve hand rehabilitation for dexterous manipulation. The key innovation is the use of topological scans of a user's hand to create a custom-fit glove via silicone mold casting, moving beyond standardized measurements that often limit effectiveness. Finite element analysis (FEA) simulates actuator bending and contact forces from physical human-robot interaction (pHRI), providing insights into joint mobilization and pressure distribution. The researchers also conducted pneumatic pressure control experiments to flex the user's finger with both static and dynamic references, demonstrating accurate and targeted mobility of the metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints while accounting for intrinsic stiffness.

The paper, accepted for the IEEE RAS/EMBS 11th International Conference on Biomedical Robotics and Biomechatronics (BioRob 2026), includes testing of multiple designs. Relaxing the strain-limiting layer improved actuator-to-finger joint alignment during actuation. The work establishes a framework for optimizing exoglove design by personalizing structural conformability, joint topology, pHRI contact modeling, and time-dependent actuation-deformation profiles. This lays groundwork for enabling assistance in dexterous manipulation and neuromuscular rehabilitation of fine motor skills, potentially benefiting patients recovering from stroke, spinal cord injury, or other motor impairments.