Human-Robot Interactions - Estimating Force Feedback of the Human Wrist
Abstract This research is part of the MSK-TIM (Musculoskeletal Tele-robotic Imaging Machine) project. The MSK-TIM is a device which facilitates remote ultrasound diagnosis through remote control of an ultrasound probe. The work reported here is to facilitate human-robot interaction in remote ultrasound imaging; the research work tried to incorporate force feedback into MSK-TIM device such that the operator (radiologist specialist) can examine the patient more efficiently, remotely. Understanding and modeling the biomechanics of the wrist is important in several fields including medicine and robotics. Up to now, most models have been developed to accurately represent the internal structure of the wrist. However, these models are complex and computationally time-consuming. This study reports on investigation on how we developed a time-efficient finite element model of the wrist. The material parameters were derived using model-correction finite element analysis. The force-displacement experimental measurements were taken at three areas of the human wrist using a robotic tele-sonography manipulator. The finite element model was then incrementally modified to improve the computational time, while measuring the corresponding error. The finite element model successfully predicted force feedback from the human arm with a small computation time (18.4s) per step. The subsequent finite element models further reduced this time (0.95s). As a result of this study, it has been shown that soft tissues can be generalized as a phenomenological material to decrease model complexity. Finally, the geometry of anatomy can be simplified, without a major reduction in accuracy, to greatly reduce the computation time — as shown by the 95% reduction in this study.
Authors
- Reza Fotouhi (ORCID: https://orcid.org/0000-0002-3972-1468)
- Haron Obaid (ORCID: https://orcid.org/0000-0001-6223-6818)
- Zachary Ochitwa (ORCID: https://orcid.org/0009-0006-0526-6002)
Institutions
- University of Saskatchewan (CA)
Publication Details
- Published
- 2024-08-25
- DOI
- https://doi.org/10.1115/detc2024-143745
- Primary Topic
- Robot Manipulation and Learning
- Type
- article
- Field-Weighted Citation Impact
- 0.00