@article {550, title = {Effect of exoskeleton-based weight support on upper extremities functional tasks}, journal = {Authorea Preprints}, year = {2024}, author = {Bucchieri, Anna and Khair, Alexander and Laffranchi, Matteo and Deshpande, Ashish} } @conference {546, title = {Effects of wearing an upper extremity exoskeleton on measuring joint kinematics during standardized clinical assessment tasks}, booktitle = {2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, year = {2024}, publisher = {IEEE}, organization = {IEEE}, author = {Hong, Yoon No Gregory and Kim, Kyoungsoon and Deshpande, Ashish D and Roh, Jinsook} } @article {534, title = {Experimental and Simulation-Based Estimation of Interface Power During Physical Human-Robot Interaction in Hand Exoskeletons}, journal = {IEEE Robotics and Automation Letters}, volume = {9}, year = {2024}, month = {March}, pages = {2575-2581}, abstract = {

Even the best wearable robots face challenges with power losses in the system, especially at the physical attachment interface. While some sources for power loss are inherent to the system, such as human soft tissue or musculoskeletal joint damping, other sources such as soft padding materials and bias strap forces can be modulated to optimize interface power transmission. Few methods currently exist for estimating power loss at physical human-robot interfaces, especially for upper-body exoskeletons. This letter presents a novel method to estimate interface power from experimental data in a wearable hand device, along with a simulation model for predicting interaction behavior by incorporating viscoelastic properties at the attachment interface. The experimental method is implemented with the Maestro hand exoskeleton, and repeatability of the interface power estimation is confirmed with pilot human testing. Simulation results are compared with experimental estimation of interface power, showing agreement of trends and validating the use of a simulation model to predict physical human-robot interaction behavior. These findings highlight the advantages of multi-body simulations as a tool to perform modular, inexpensive, and predictive investigations in physical human-robot interaction, without affecting the real-world mechatronic system or hindering the subject{\textquoteright}s safety. The proposed tools for experimental estimation of interface power and simulation modeling can optimize the design and control of robots for seamless integration with the human body.

}, keywords = {design and human factors, Estimation, Exoskeletons, human-centered robotics, Physical human-robot interaction, Power measurement, Robots, Sea measurements, Torque, Torque measurement, wearable robotics}, issn = {2377-3766}, doi = {10.1109/LRA.2023.3326679}, author = {Yousaf, Saad N. and Mukherjee, Gaurav and King, Raymond and Deshpande, Ashish D.} } @article {545, title = {Kinematic Performance of a Customizable Single Degree-of-Freedom Gait Trainer for Cost-Effective Therapy Aimed at Neuromuscular Impairments}, journal = {Journal of Medical Devices}, volume = {18}, year = {2024}, pages = {011003}, author = {Jadav, Shail and Karvaje, Karthik Subramanya and Kadam, Sujay D and Vashista, Vineet and Sulzer, James and Deshpande, Ashish and Palanthandalam-Madapusi, Harish J} } @conference {533, title = {Characterizing the Onset and Offset of Motor Imagery During Passive Arm Movements Induced by an Upper-Body Exoskeleton}, booktitle = {2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023}, month = {Oct}, abstract = {

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.

}, keywords = {Decoding, Exoskeletons, Light emitting diodes, Manipulators, Neurorehabilitation, Performance evaluation, Robot sensing systems}, doi = {10.1109/IROS55552.2023.10342492}, author = {Mitra, Kanishka and Racz, Frigyes Samuel and Kumar, Satyam and Deshpande, Ashish D. and Del R. Mill{\'a}n, Jos{\'e}} } @booklet {529, title = {Differential Spiral Joint Mechanism for Coupled Variable Stiffness Actuation}, year = {2023}, abstract = {

In this study, we present the Differential Spiral Joint (DSJ) mechanism for variable stiffness actuation in tendon-driven robots. The DSJ mechanism semi-decouples the modulation of position and mechanical stiffness, allowing independent trajectory tracking in different parameter space. Past studies show that increasing the mechanical stiffness achieves the wider range of renderable stiffness, whereas decreasing the mechanical stiffness improves the quality of actuator decoupling and shock absorbance. Therefore, it is often useful to modulate the mechanical stiffness to balance the required level of stiffness and safety. In addition, the DSJ mechanism offers a compact form factor, which is suitable for applications where the size and weight are important. The performance of the DSJ mechanism in various areas is validated through a set of experiments.

}, url = {https://arxiv.org/abs/2301.09158}, author = {Mincheol Kim and Ashish 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 {526, title = {SCAPE: Learning Stiffness Control from Augmented Position Control Experiences}, booktitle = {Proceedings of the 5th Conference on Robot Learning}, year = {2022}, month = {08{\textendash}11 Nov}, publisher = {PMLR}, organization = {PMLR}, abstract = {

We introduce a sample-efficient method for learning state-dependent stiffness control policies for dexterous manipulation. The ability to control stiffness facilitates safe and reliable manipulation by providing compliance and robustness to uncertainties. Most current reinforcement learning approaches to achieve robotic manipulation have exclusively focused on position control, often due to the difficulty of learning high-dimensional stiffness control policies. This difficulty can be partially mitigated via policy guidance such as imitation learning. However, expert stiffness control demonstrations are often expensive or infeasible to record. Therefore, we present an approach to learn Stiffness Control from Augmented Position control Experiences (SCAPE) that bypasses this difficulty by transforming position control demonstrations into approximate, suboptimal stiffness control demonstrations. Then, the suboptimality of the augmented demonstrations is addressed by using complementary techniques that help the agent safely learn from both the demonstrations and reinforcement learning. By using simulation tools and experiments on a robotic testbed, we show that the proposed approach efficiently learns safe manipulation policies and outperforms learned position control policies and several other baseline learning algorithms.

}, url = {https://proceedings.mlr.press/v164/kim22b.html}, author = {Kim, Mincheol and Niekum, Scott and Deshpande, Ashish D.}, editor = {Faust, Aleksandra and Hsu, David and Neumann, Gerhard} } @article {452, title = {Balancing Stability and Stiffness Through the Optimization of Parallel Compliance: Using Coupled Tendon Routing}, journal = {IEEE Robotics \& Automation Magazine}, volume = {28}, year = {2021}, month = {06/2021}, pages = {14-22}, author = {Mincheol Kim and Ashish D. Deshpande} } @conference {481, title = {SCAPE: Learning Stiffness Control from Augmented Position Control Experiences}, booktitle = {Conference on Robot Learning. Proceedings of Machine Learning Research}, year = {2021}, author = {Mincheol Kim and Scott Niekum and Ashish Deshpande} } @article {379, title = {Assessing Wrist Movement with Robotic Devices}, journal = {IEEE Transactions on Neural Systems \& Rehabilitation Engineering}, year = {2018}, author = {Chad G. Rose and Evan Pezent and Claudia K. Kanny and Ashish D. Deshpande and Marcia K. O{\textquoteright}Malley} } @conference {378, title = {Designing Variable Stiffness Profiles To Optimize The Physical Human Robot Interface Of Hand Exoskeletons}, booktitle = {BioRob (IEEE International Conference on Biomedical Robotics and Biomechatronics)}, year = {2018}, author = {Varghese, R. J. and Mukherjee, G. and King, R. and Keller, S. and Deshpande, A. D.} } @conference {318, title = {Development and validation of modeling framework for interconnected tendon networks in robotic and human fingers}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, year = {2017}, publisher = {IEEE}, organization = {IEEE}, author = {Niehues, Taylor D and King, Raymond J and Deshpande, Ashish D and Keller, Sean} } @conference {349, title = {Estimating Anatomical Wrist Joint Motion with a Robotic Exoskeleton}, booktitle = {IEEE International Conference on Rehabilitation Robotics (ICORR)}, year = {2017}, author = {Rose, C. G. and Kann, C. K. and Deshpande, A. D. and O{\textquoteright}Malley, M. K.} } @article {332, title = {An Upper-Body Rehabilitation Exoskeleton Harmony with an Anatomical Shoulder Mechanism: Design, Modeling, Control, and Performance Evaluation}, journal = {International Journal of Robotics Research (IJRR)}, volume = {36}, year = {2017}, pages = {414-435}, author = {Bongsu Kim and Ashish D. Deshpande} } @article {290, title = {Control in the Reliable Region of a Statistical Model}, journal = {Transactions on Robotics}, volume = {32}, year = {2016}, chapter = {1017}, author = {Yun, Y. and J. Ko and Deshpande, A.D.} } @article {kuo2015design, title = {Design and performance of a motor-driven mechanism to conduct experiments with the human index finger}, journal = {Journal of Mechanisms and Robotics}, volume = {7}, number = {3}, year = {2015}, pages = {031010}, publisher = {American Society of Mechanical Engineers}, author = {Kuo, Pei-Hsin and Hayes, Jerod and Deshpande, Ashish D.} } @article {kuo2015novel, title = {A Novel Joint Design for Robotic Hands with Human-Like Nonlinear Compliance}, journal = {Journal of Mechanisms and Robotics}, year = {2015}, author = {Kuo, Pei-Hsin and Deshpande, A.D.} } @article {kim2014design, title = {Design of Nonlinear Rotational Stiffness Using a Noncircular Pulley-Spring Mechanism}, journal = {Journal of Mechanisms and Robotics}, volume = {6}, number = {4}, year = {2014}, pages = {041009}, publisher = {American Society of Mechanical Engineers}, author = {Kim, Bongsu and Deshpande, Ashish D.} } @conference {Kim:DSCC:2014, title = {Impedance Control Based on a Position Sensor in a Rehabilitation Robot}, booktitle = {ASME Dynamic Systems and Control Conference (DSCC)}, year = {2014}, author = {Kim, Bongsu and Rodot, Aurelien and Deshpande, A.D.} } @article {Kim:WeRob:2014, title = {Mechanical Design of A Wearable Robot for Upper Body Rehabilitation}, journal = {International Workshop on Wearable Robotics (WeRob)}, year = {2014}, author = {Kim, Bongsu and Deshpande, Ashish D.} } @article {yun2014statistical, title = {Statistical method for prediction of gait kinematics with Gaussian process regression}, journal = {Journal of Biomechanics}, volume = {47}, number = {1}, year = {2014}, pages = {186{\textendash}192}, publisher = {Elsevier}, author = {Yun, Youngmok and Kim, Hyun-Chul and Shin, Sung Yul and Lee, Junwon and Deshpande, Ashish D. and Kim, Changhwan} } @article {deshpande2013control, title = {Control strategies for the index finger of a tendon-driven hand}, journal = {The International Journal of Robotics Research}, volume = {32}, number = {1}, year = {2013}, pages = {115{\textendash}128}, publisher = {SAGE Publications}, author = {Deshpande, Ashish D. and Ko, Jonathan and Fox, Dieter and Matsuoka, Yoky} } @conference {Agarwal:ASB:2013, title = {Integration of Musculoskeletal Analysis with Engineering Design for Virtual Prototyping of Exoskeletons}, booktitle = {American Society of Biomechanics}, year = {2013}, author = {Agarwal, Priyanshu and Kuo, Pei-Hsin and Neptune, Richard and Deshpande, Ashish D.} } @article {deshpande2013mechanisms, title = {Mechanisms of the anatomically correct testbed hand}, journal = {Mechatronics, IEEE/ASME Transactions on}, volume = {18}, number = {1}, year = {2013}, pages = {238{\textendash}250}, publisher = {IEEE}, author = {Deshpande, Ashish D. and Xu, Zhe and Weghe, Michael J Vande and Brown, Benjamin H and Ko, Jonathan and Chang, Lillian Y and Wilkinson, David D and Bidic, Sean M and Matsuoka, Yoky} } @conference {kuo2013novel, title = {Novel design of a passive variable stiffness joint mechanism: Inspiration from biomechanics of hand joints}, booktitle = {ASME 2013 Dynamic Systems and Control Conference}, year = {2013}, pages = {V002T28A003{\textendash}V002T28A003}, publisher = {American Society of Mechanical Engineers}, organization = {American Society of Mechanical Engineers}, author = {Kuo, Pei-Hsin and Deshpande, Ashish D.} } @conference {agarwal2013novel, title = {A novel framework for virtual prototyping of rehabilitation exoskeletons}, booktitle = {Rehabilitation Robotics (ICORR), 2013 IEEE International Conference on}, year = {2013}, pages = {1{\textendash}6}, publisher = {IEEE}, organization = {IEEE}, author = {Agarwal, Priyanshu and Kuo, Pei-Hsin and Neptune, Richard and Deshpande, Ashish D.} } @article {kuo2012muscle, title = {Muscle-tendon units provide limited contributions to the passive stiffness of the index finger metacarpophalangeal joint}, journal = {Journal of biomechanics}, volume = {45}, number = {15}, year = {2012}, pages = {2531{\textendash}2538}, publisher = {Elsevier}, author = {Kuo, Pei-Hsin and Deshpande, Ashish D.} } @conference {kuo2011design, title = {Design of a motor-driven mechanism to conduct experiments to determine the passive joint properties of the human index finger}, booktitle = {ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference}, year = {2011}, pages = {615{\textendash}622}, publisher = {American Society of Mechanical Engineers}, organization = {American Society of Mechanical Engineers}, author = {Kuo, Pei-Hsin and Hayes, Jerod and Deshpande, Ashish D.} } @conference {Kuo:ASB:2011, title = {Effects of Variations in Velocity on Viscoelastic Behaviour in the Metacarpophalangeal (MCP) Joint of the Human Hand}, booktitle = {In Proceedings of American Society of Biomechanics}, year = {2011}, author = {Kuo, Pei-Hsin and Deshpande, Ashish D.} } @article {deshpande2010acquiring, title = {Acquiring variable moment arms for index finger using a robotic testbed}, journal = {Biomedical Engineering, IEEE Transactions on}, volume = {57}, number = {8}, year = {2010}, pages = {2034{\textendash}2044}, publisher = {IEEE}, author = {Deshpande, Ashish D. and Balasubramanian, Ravi and Ko, Jonathan and Matsuoka, Yoky} } @conference {kuo2010contribution, title = {Contribution of passive properties of muscle-tendon units to the metacarpophalangeal joint torque of the index finger}, booktitle = {Biomedical Robotics and Biomechatronics (BioRob), 2010 3rd IEEE RAS and EMBS International Conference on}, year = {2010}, pages = {288{\textendash}294}, publisher = {IEEE}, organization = {IEEE}, author = {Kuo, Pei-Hsin and Deshpande, Ashish D.} } @conference {deshpande2009act, title = {ACT hand finger control: muscle and joint torque control strategies}, booktitle = {IEEE International Conference on Robotics and Automation}, year = {2009}, author = {Deshpande, Ashish D. and Ko, Jonathan and Fox, Dieter and Matsuoka, Yoky} }