02761nas a2200301   4500008004100000022001400041245010800055210006900163260000800232300001200240490000700252520175000259653002102009653002902030653001702059653002502076653001502101653001902116653003402135653002602169653002802195653002602223653002402249653001302273100002502286700002602311856012202337       2023                        eng d  a2168-230500aAssessment of Upper-Body Movement Quality in the Cartesian-Space is Feasible in the Harmony Exoskeleton0 aAssessment of UpperBody Movement Quality in the CartesianSpace i  cDec  a985-9950 v533 a<p>To determine the most effective interventions for poststroke patients, it is imperative to monitor the recovery process. Robotic exoskeletons' built-in sensing capabilities enable accurate kinematic measurement with no additional setup time. Although position sensors used in exoskeletons are accurate, a mismatch between the robot's and the human's joints can lead to inaccurate measurements. In addition, the robot's residual dynamics can interfere with human's natural movements and the kinematic metrics assessed in the robot would not be representative of the human's movement in free-motion. So far, the accuracy of robotic exoskeletons in assessing upper-body kinematics has not been verified. The bilateral upper-body Harmony exoskeleton has features favorable to minimize joint misalignments and the robot's residual dynamics. In this study, we examined Harmony's ability to accurately assess Cartesian-space kinematic parameters associated with the wearer's movement quality. We analyzed data collected from eight healthy participants that executed point-to-point movements with and without the presence of the robot and at fast and slow speeds. Ground truth was acquired with an optical motion capture, and we extracted the kinematic parameters from the measured data. The results suggest that Harmony can accurately measure kinematic parameters associated with movement quality, and these parameters could appropriately reflect wearer's natural movements at a slow speed. Therefore, Harmony could aid the evaluation of the effectiveness of different interventions, which is more sensitive and efficient than currently adopted clinical outcomes. This allows for individualization of a treatment plan and a detailed follow-up.</p>
10aAssistive robots10aAtmospheric measurements10aExoskeletons10aKinematic assessment10aKinematics10aMotion capture10aOptical variables measurement10aParticle measurements10arehabilitation robotics10aRobot sensing systems10arobotic exoskeleton10aTraining1 aDe Oliveira, Ana, C.1 aDeshpande, Ashish, D.  uhttps://reneu.robotics.utexas.edu/assessment-upper-body-movement-quality-cartesian-space-feasible-harmony-exoskeleton02640nas a2200241   4500008004100000245012300041210006900164260000800233520175800241653001301999653001702012653002602029653001702055653002402072653002702096653002602123100002002149700002602169700001802195700002602213700002702239856013202266       2023                        eng d00aCharacterizing the Onset and Offset of Motor Imagery During Passive Arm Movements Induced by an Upper-Body Exoskeleton0 aCharacterizing the Onset and Offset of Motor Imagery During Pass  cOct3 a<p>Two distinct technologies have gained attention lately due to their prospects for motor rehabilitation: robotics and brain-machine interfaces (BMIs). Harnessing their combined efforts is a largely uncharted and promising direction that has immense clinical potential. However, a significant challenge is whether motor intentions from the user can be accurately detected using non-invasive BMIs in the presence of instrumental noise and passive movements induced by the rehabilitation exoskeleton. As an alternative to the straight-forward continuous control approach, this study instead aims to characterize the onset and offset of motor imagery during passive arm movements induced by an upper-body exoskeleton to allow for the natural control (initiation and termination) of functional movements. Ten participants were recruited to perform kinesthetic motor imagery (MI) of the right arm while attached to the robot, simultaneously cued with LEDs indicating the initiation and termination of a goal-oriented reaching task. Using electroencephalogram signals, we built a decoder to detect the transition between i) rest and beginning MI and ii) maintaining and ending MI. Offline decoder evaluation achieved group average onset accuracy of 60.7% and 66.6% for offset accuracy, revealing that the start and stop of MI could be identified while attached to the robot. Furthermore, pseudo-online evaluation could replicate this performance, forecasting reliable online exoskeleton control in the future. Our approach showed that participants could produce quality and reliable sensorimotor rhythms regardless of noise or passive arm movements induced by wearing the exoskeleton, which opens new possibilities for BMI control of assistive devices.</p>
10aDecoding10aExoskeletons10aLight emitting diodes10aManipulators10aNeurorehabilitation10aPerformance evaluation10aRobot sensing systems1 aMitra, Kanishka1 aRacz, Frigyes, Samuel1 aKumar, Satyam1 aDeshpande, Ashish, D.1 aMillán, José, Del R.  uhttps://reneu.robotics.utexas.edu/characterizing-onset-and-offset-motor-imagery-during-passive-arm-movements-induced-upper-body