@article {530,
	title = {Kinematic coordinations capture learning during human-exoskeleton interaction},
	journal = {Scientific Reports},
	volume = {13},
	year = {2023},
	month = {06/2023},
	abstract = {<p><span style="color: rgb(34, 34, 34); font-family: -apple-system, BlinkMacSystemFont, \&quot;Segoe UI\&quot;, Roboto, Oxygen-Sans, Ubuntu, Cantarell, \&quot;Helvetica Neue\&quot;, sans-serif; font-size: 18px;">Human{\textendash}exoskeleton interactions have the potential to bring about\&nbsp;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.</span></p>
},
	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 {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 {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 {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}
}
