MORE | Fall 2022
Quantification of Shoulder Joint Impedance Based on Various Arm Postures
In this study, we utilize a shoulder exoskeleton robot, which makes use of a 4-bar spherical parallel manipulator (4B-SPM) that has inherently low inertia and as a result can provide fast perturbations. By employing a parallel architecture, the 4B-SPM exoskeleton has the advantage of high acceleration, fast enough to satisfy the speed requirement for the characterization of distinct neuromuscular properties of the shoulder. We aim to characterize shoulder joint stiffness by providing filtered gaussian random perturbations with RMS value, frequency of 2 degrees and 3 Hz respectively in the yaw direction. These perturbations are captured by Vicon Motion capture system by placing markers on the arm brace, which allows the arm to be locked at a particular pose and is attached to the system-end effector. Torque is measured using a force-torque (FT) sensor at 15 different arm postures. The stiffness characterization is done by utilizing the short data segment (SDS) method of time-varying system identification. Understanding these properties of the shoulder control could assist in the development of stable controller systems, various human-robot interaction algorithms, preparation of diagnostic cases for patients and the study of occupational illnesses related to vibration transmission from power hand tools.
Hometown: Tempe, Arizona, United States
Graduation date: Fall 2022