@conference {531, title = {An Extended Virtual Proxy Haptic Algorithm for Dexterous Manipulation in Virtual Environments}, booktitle = {2023 IEEE World Haptics Conference (WHC)}, year = {2023}, month = {July}, abstract = {

With the evolution of hand-based haptic interfaces, novel forms of controlled force feedback arise allowing multipoint interaction between virtual objects and the hand{\textquoteright}s digits. With these advances, there must be an effective force display coupled with an intuitive visualization of the hand at its points of high manipulability. This is the basis for dexterous manipulation immersion in virtual environments. Still, there are challenges due to the complexity of force interaction, bandwidth limitations, and redundant visual configurations. In this paper, we present an extended proxy algorithm for digit-based interactions, which through configuration-based optimization, provides an efficient, robust, and visually plausible way to interact with virtual objects with a virtual hand. Additionally, we revisit a seldom-seen modality of haptic rendering, whole-hand kinesthetic feedback, with the Maestro exoskeleton in the implementation of our algorithm. We unify these methods and develop a CHAI3D module in a comprehensive visuo-haptic framework that was evaluated through demonstrations of joint-level haptic force data during interaction with static and dynamic objects alike. Our computationally-efficient approach sets the foundation for the visual display of in-hand virtual object manipulation with the effective rendering of stable haptic interactions under complex tasks.

}, keywords = {Exoskeleton, Exoskeletons, Force, Force feedback, Haptics, Heuristic algorithms, Rendering (computer graphics), Simulation, Virtual environments, Virtual Reality, Visualization}, doi = {10.1109/WHC56415.2023.10224434}, author = {Galvan, Aldo F. and Ramirez, Job D. and Deshpande, Ashish D. and Fey, Ann Majewicz} } @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} } @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 {497, title = {Dynamic Finger Task Identification Using Electromyography}, booktitle = {IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)}, year = {2022}, author = {Paria Esmatloo and Keya Ghonasgi and Raymond King and Ashish D. Deshpande} } @conference {498, title = {Human-Robot Interaction: Muscle Activation and Angular Location Affect Soft Tissue Stiffness}, booktitle = {IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)}, year = {2022}, author = {Saad N. Yousaf and Keya Ghonasgi and Paria Esmatloo and Ashish D. Deshpande} } @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 {496, title = {Impact of Gravity Compensation on Upper Extremity Movements in Harmony Exoskeleton}, booktitle = {International Conference on Rehabilitation Robotics (ICORR)}, year = {2022}, author = {Rhet O. Hailey and Ana C. de Oliveira and Keya Ghonasgi and R. Whitford and R. Lee and Chad G. Rose and Ashish D. Deshpande} } @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.} } @conference {467, title = {An Actuated Indenter for Characterization of Soft Tissue Towards Human-Centered Design}, booktitle = { IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)}, year = {2021}, author = {Saad N. Yousaf and Keya Ghonasgi and Paria Esmatloo and Ashish D. Deshpande} } @conference {473, title = {Capturing Skill State in Curriculum Learning for Human Skill Acquisition}, booktitle = {2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021)}, year = {2021}, author = {Keya Ghonasgi and Reuth Mirsky and Sanmit Narvekar and Bharath Masetty and Adrian Haith and Peter Stone and Ashish Deshpande} } @conference {465, title = {Design and Validation of a Novel Exoskeleton Hand Interface: The Eminence Grip}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, year = {2021}, author = {Keya Ghonasgi and Chad G. Rose and Ana C. de Oliveira and Rohit J. Varghese and Ashish D. Deshpande} } @conference {466, title = {A Method for the Analysis of Physical Human Robot Interaction}, booktitle = { IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)}, year = {2021}, author = {Saad N. Yousaf and Paria Esmatloo and Keya Ghonasgi and Ashish D. Deshpande} } @article {455, title = {A Modular Design for Distributed Measurement of Human-Robot Interaction Forces in Wearable Devices}, journal = {Sensors}, year = {2021}, author = {Keya Ghonasgi and Saad N. Yousaf and Paria Esmatloo and Ashish D. Deshpande} } @conference {449, title = {Design and Validation of a Sensorized Physical Human-Robot Interface for Distributed Force Measurement}, booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Workshop on Ergonomic Human-Robot Collaboration: Opportunities and Challenges}, year = {2020}, author = {Keya Ghonasgi and Saad N. Yousaf and Paria Esmatloo and Ruth Heckler and Job Ramirez and Chad G. Rose and Ashish D. Deshpande} } @proceedings {432, title = {A Novel Inverse Kinematics Method for Upper-Limb Exoskeleton under Joint Coordination Constraints}, year = {2020}, author = {Stefano D. Gasperina and Keya Ghonasgi and Ana C. de Oliveira and Ashish D. Deshpande} } @conference {411, title = {Estimating the Effect of Robotic Intervention on Elbow Joint Motion}, booktitle = { IEEE International Symposium on Robot and Human Interactive Communication }, year = {2019}, month = {09/2019}, author = {Keya Ghonasgi and Ana C. de Oliveira and Anna Shafer and Chad G. Rose and Ashish D. Deshpande} } @article {deshpande2012contributions, title = {Contributions of intrinsic visco-elastic torques during planar index finger and wrist movements}, journal = {Biomedical Engineering, IEEE Transactions on}, volume = {59}, number = {2}, year = {2012}, pages = {586{\textendash}594}, publisher = {IEEE}, author = {Deshpande, Ashish D. and Gialias, Nick and Matsuoka, Yoky} }