@article {537, title = {Assessment of Upper-Body Movement Quality in the Cartesian-Space is Feasible in the Harmony Exoskeleton}, journal = {IEEE Transactions on Human-Machine Systems}, volume = {53}, year = {2023}, month = {Dec}, pages = {985-995}, abstract = {

To determine the most effective interventions for poststroke patients, it is imperative to monitor the recovery process. Robotic exoskeletons{\textquoteright} 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{\textquoteright}s and the human{\textquoteright}s joints can lead to inaccurate measurements. In addition, the robot{\textquoteright}s residual dynamics can interfere with human{\textquoteright}s natural movements and the kinematic metrics assessed in the robot would not be representative of the human{\textquoteright}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{\textquoteright}s residual dynamics. In this study, we examined Harmony{\textquoteright}s ability to accurately assess Cartesian-space kinematic parameters associated with the wearer{\textquoteright}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{\textquoteright}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.

}, keywords = {Assistive robots, Atmospheric measurements, Exoskeletons, Kinematic assessment, Kinematics, Motion capture, Optical variables measurement, Particle measurements, rehabilitation robotics, Robot sensing systems, robotic exoskeleton, Training}, issn = {2168-2305}, doi = {10.1109/THMS.2023.3305391}, author = {De Oliveira, Ana C. and Deshpande, Ashish D.} } @conference {532, title = {A Novel Control Law for Multi-Joint Human-Robot Interaction Tasks While Maintaining Postural Coordination}, booktitle = {2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023}, month = {Oct}, abstract = {

Exoskeleton robots are capable of safe torque-controlled interactions with a wearer while moving their limbs through predefined trajectories. However, affecting and assisting the wearer{\textquoteright}s movements while incorporating their inputs (effort and movements) effectively during an interaction re-mains an open problem due to the complex and variable nature of human motion. In this paper, we present a control algorithm that leverages task-specific movement behaviors to control robot torques during unstructured interactions by implementing a force field that imposes a desired joint angle coordination behavior. This control law, built by using principal component analysis (PCA), is implemented and tested with the Harmony exoskeleton. We show that the proposed control law is versatile enough to allow for the imposition of different coordination behaviors with varying levels of impedance stiffness. We also test the feasibility of our method for unstructured human-robot interaction. Specifically, we demonstrate that participants in a human-subject experiment are able to effectively perform reaching tasks while the exoskeleton imposes the desired joint coordination under different movement speeds and interaction modes. Survey results further suggest that the proposed control law may offer a reduction in cognitive or motor effort. This control law opens up the possibility of using the exoskeleton for training the participating in accomplishing complex multi-joint motor tasks while maintaining postural coordination.

}, keywords = {Behavioral sciences, Exoskeletons, Human-robot interaction, Robot kinematics, Surveys, Training, Trajectory}, doi = {10.1109/IROS55552.2023.10342501}, author = {Ghonasgi, Keya and Mirsky, Reuth and Haith, Adrian M. and Stone, Peter and Deshpande, Ashish D.} } @conference {527, title = {Impact of Gravity Compensation on Upper Extremity Movements in Harmony Exoskeleton}, booktitle = {2022 International Conference on Rehabilitation Robotics (ICORR)}, year = {2022}, month = {July}, abstract = {

Robots have been used to offset the limb weight through gravity compensation in upper body rehabilitation to delineate the effects of loss of strength and loss of dexterity, which are two common forms of post-stroke impairments. In this paper, we explored the impact of this anti-gravity support on the quality of movement during reaching and coordinated arm movements in a pilot study with two participants with chronic stroke. The subjects donned the Harmony exoskeleton which supported proper shoulder coordination in addition to providing gravity compensation. Participants had previously taken part in seven one-hour sessions with the Harmony exoskeleton, performing six sets of passive-stretching and active exercises. Pre- and post-training sessions included assessments of two separate tasks, planar reaching and a set of six coordinated arm movements, in two conditions, outside of and supported by the exoskeleton. The movements were recorded using an optical motion capture system and analyzed using spectral arc length (SPARC) and straight line deviation to quantify movement smoothness and quality. We observed that gravity compensation resulted in an increased smoothness for the subject with high level of impairment whereas compensation resulted in a reduction in smoothness for the subject with low level of impairment in the reaching task. Both participants demonstrated better coordination of the shoulder-arm joint with gravity compensation. This result motivates further studies into the role of gravity compensation during coordinated movement training and rehabilitation interventions.

}, keywords = {Exoskeletons, Neural pathways, Optical feedback, Optical recording, Robot kinematics, Shoulder, Training}, doi = {10.1109/ICORR55369.2022.9896415}, author = {Hailey, Rhet O. and De Oliveira, Ana C. and Ghonasgi, Keya and Whitford, Bob and Lee, Robert K. and Rose, Chad G. and Deshpande, Ashish D.} } @conference {528, title = {Quantifying Changes in Kinematic Behavior of a Human-Exoskeleton Interactive System}, booktitle = {2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2022}, month = {Oct}, abstract = {

While human-robot interaction studies are becoming more common, quantification of the effects of repeated interaction with an exoskeleton remains unexplored. We draw upon existing literature in human skill assessment and present extrinsic and intrinsic performance metrics that quantify how the human-exoskeleton system{\textquoteright}s behavior changes over time. Specifically, in this paper, we present a new performance metric that provides insight into the system{\textquoteright}s kinematics associated with {\textquoteleft}successful{\textquoteright} movements resulting in a richer characterization of changes in the system{\textquoteright}s behavior. A human subject study is carried out wherein participants learn to play a challenging and dynamic reaching game over multiple attempts, while donning an upper-body exoskeleton. The results demonstrate that repeated practice results in learning over time as identified through the improvement of extrinsic performance. Changes in the newly developed kinematics-based measure further illumi-nate how the participant{\textquoteright}s intrinsic behavior is altered over the training period. Thus, we are able to quantify the changes in the human-exoskeleton system{\textquoteright}s behavior observed in relation with learning.

}, keywords = {Atmospheric measurements, Exoskeletons, Games, Human-robot interaction, Interactive systems, Kinematics, Training}, doi = {10.1109/IROS47612.2022.9981032}, author = {Ghonasgi, Keya and Mirsky, Reuth and Haith, Adrian M. and Stone, Peter and Deshpande, Ashish D.} }