00761nas a2200181 4500008004100000245014400041210006900185300001100254490000700265100001700272700003300289700002000322700002100342700001800363700002200381700003900403856013700442 2024 eng d00aKinematic Performance of a Customizable Single Degree-of-Freedom Gait Trainer for Cost-Effective Therapy Aimed at Neuromuscular Impairments0 aKinematic Performance of a Customizable Single DegreeofFreedom G a0110030 v181 aJadav, Shail1 aKarvaje, Karthik, Subramanya1 aKadam, Sujay, D1 aVashista, Vineet1 aSulzer, James1 aDeshpande, Ashish1 aPalanthandalam-Madapusi, Harish, J uhttps://reneu.robotics.utexas.edu/kinematic-performance-customizable-single-degree-freedom-gait-trainer-cost-effective-therapy-aimed02543nas a2200181 4500008004100000245008200041210006900123260001200192490000700204520197300211100001902184700001802203700002002221700002202241700001702263700002602280856005502306 2023 eng d00aKinematic coordinations capture learning during human-exoskeleton interaction0 aKinematic coordinations capture learning during humanexoskeleton c06/20230 v133 a

Human–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–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–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–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.

1 aGhonasgi, Keya1 aMirsky, Reuth1 aBhargava, Nisha1 aHaith, Adrian, M.1 aStone, Peter1 aDeshpande, Ashish, D. uhttps://www.nature.com/articles/s41598-023-35231-301933nas a2200145 4500008004100000245007500041210006900116520140200185100001901587700001401606700002001620700002601640700001801666856010301684 2021 eng d00aKinematic comparison of single degree-of-freedom robotic gait trainers0 aKinematic comparison of single degreeoffreedom robotic gait trai3 a

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 ±
0.17 cm, mean ± SD) than the four- (6.84 ± 0.37 cm) and six-bar (7.15 ± 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.

1 aLee, Jeonghwan1 aLi, Lailu1 aShin, Sung, Yul1 aDeshpande, Ashish, D.1 aSulzer, James uhttps://reneu.robotics.utexas.edu/kinematic-comparison-single-degree-freedom-robotic-gait-trainers00579nas a2200133 4500008004100000245008000041210006900121260004500190300003000235100002300265700002200288700002600310856010900336 2013 eng d00aKinematics and Dynamics of a Biologically Inspired Index Finger Exoskeleton0 aKinematics and Dynamics of a Biologically Inspired Index Finger  bAmerican Society of Mechanical Engineers aV002T28A001–V002T28A0011 aAgarwal, Priyanshu1 aHechanova, Arnold1 aDeshpande, Ashish, D. uhttps://reneu.robotics.utexas.edu/kinematics-and-dynamics-biologically-inspired-index-finger-exoskeleton