@article {530, title = {Kinematic coordinations capture learning during human-exoskeleton interaction}, journal = {Scientific Reports}, volume = {13}, year = {2023}, month = {06/2023}, abstract = {

Human{\textendash}exoskeleton interactions have the potential to bring about\ changes in human behavior for physical rehabilitation or skill augmentation. Despite significant advances in the design and control of these robots, their application to human training remains limited. The key obstacles to the design of such training paradigms are the prediction of human{\textendash}exoskeleton interaction effects and the selection of interaction control to affect human behavior. In this article, we present a method to elucidate behavioral changes in the human{\textendash}exoskeleton system and identify expert behaviors correlated with a task goal. Specifically, we observe the joint coordinations of the robot, also referred to as kinematic coordination behaviors, that emerge from human{\textendash}exoskeleton interaction during learning. We demonstrate the use of kinematic coordination behaviors with two task domains through a set of three human-subject studies. We find that participants (1) learn novel tasks within the exoskeleton environment, (2) demonstrate similarity of coordination during successful movements within participants, (3) learn to leverage these coordination behaviors to maximize success within participants, and (4) tend to converge to similar coordinations for a given task strategy across participants. At a high level, we identify task-specific joint coordinations that are used by different experts for a given task goal. These coordinations can be quantified by observing experts and the similarity to these coordinations can act as a measure of learning over the course of training for novices. The observed expert coordinations may further be used in the design of adaptive robot interactions aimed at teaching a participant the expert behaviors.

}, url = {https://www.nature.com/articles/s41598-023-35231-3}, author = {Keya Ghonasgi and Reuth Mirsky and Nisha Bhargava and Adrian M. Haith and Peter Stone and Ashish D. Deshpande} } @article {453, title = {Kinematic comparison of single degree-of-freedom robotic gait trainers}, journal = {Mechanism and Machine Theory}, year = {2021}, abstract = {

Single degree-of-freedom mechanized gait trainers employing linkage mechanisms
can imitate the gait trajectory of the human user at a potentially lower cost than current
commercial solutions. Our goal was to evaluate recently introduced four-, six- and
eight-bar designs from the standpoint of kinematics, that is, the ability to follow a
natural gait pattern. Based on gait database of 113 healthy individuals, we synthesized
mechanisms allowing two link length adjustments and compared trajectory and path

accuracy of the ankle across mechanisms. We used cross-validation to obtain data-
independent accuracy. We used gait trajectories from a large human database and

predicted additional trajectories to represent effects of varying heights and speeds. We
found that trajectory error was significantly lower in the eight-bar mechanism (3.77 {\textpm}
0.17 cm, mean {\textpm} SD) than the four- (6.84 {\textpm} 0.37 cm) and six-bar (7.15 {\textpm} 0.48 cm)

configurations (p\<0.0001). However, we did not observe differences in time-
independent path error between configurations, or in mechanical advantage (p\>0.05).

We conclude that the eight-bar configuration is best when the mechanism is tied to a
constant velocity actuator, whereas the four-bar mechanism performs as well if the
actuator is well-controlled.

}, author = {Jeonghwan Lee and Lailu Li and Sung Yul Shin and Ashish D. Deshpande and James Sulzer} }