Enhancing assistive technology design: Biomechanical finite element modeling for grasping strategy optimization in exoskeleton data gloves
This paper endeavors to enhance the control mechanisms governing the grasping strategy of exoskeleton data gloves. A comprehensive approach was devised, integrating finite element numerical simulation techniques with the computerized control system of exoskeleton data gloves, establishing a novel, precise, and three-dimensional biomechanical finite element model of the hand. By amalgamating finite element modeling technology, biomechanical understanding, and hand kinematics, a straightforward yet efficient virtual grasping methodology was introduced to replicate three typical grasping actions dynamically. This method accurately computes contact pressure values, offering valuable insights for formulating strategies for exoskeleton data gloves. Experimental validations were conducted to ascertain the model's simulation accuracy, comparing contact pressure across all fingertips during grasping tasks demonstrating the model's high fidelity. Compared to traditional methods reliant on pressure sensors and physical measurement experiments, this approach presents several advantages, including convenience, speed, low cost, and enhanced accuracy. The proposed finite element numerical simulation model presents an optimized and viable concept for developing grasping strategies in exoskeleton data gloves, facilitating precise gripping capabilities for individuals and aiding in enhancing their capacity for independent living. Furthermore, the current model focuses on replicating typical grasping actions and may require further development to encompass a wider variety of complex object shapes and grasping maneuvers. However, this finding is crucial to the ongoing evolution of exoskeleton data glove products and systems.
Authors
- Md All Amin Newton (ORCID: https://orcid.org/0000-0002-9350-0295)
- Yan Zhang (ORCID: https://orcid.org/0009-0005-0756-7039)
- Hong Xie
Institutions
- Shanghai University of Engineering Science (CN)
Publication Details
- Journal
- Medical Engineering & Physics
- Published
- 2025-02-06
- DOI
- https://doi.org/10.1016/j.medengphy.2025.104308
- Citations
- 3
- Primary Topic
- Robot Manipulation and Learning
- Type
- article
- Field-Weighted Citation Impact
- 3.64
Funders
- Shanghai University of Engineering and Science