@article{singh_lambeth_iyer_sharma_2024, title={Dynamic Active Subspaces for Model Predictive Allocation in Over-Actuated Systems}, volume={8}, ISSN={["2475-1456"]}, url={https://doi.org/10.1109/LCSYS.2023.3342094}, DOI={10.1109/LCSYS.2023.3342094}, abstractNote={In this letter, we analyze dynamic optimization problem for robotic systems utilizing dynamic active subspaces ( $Dy\mathcal {AS}$ ) to obtain a lower-dimensional control input space by performing a global sensitivity analysis. In doing so, we set up a Model Predictive Control Allocation (MPCA) problem wherein the actuators are dynamically allocated to track a desired stabilizing torque while satisfying state and control constraints. To improve computational efficiency of the MPCA, we develop Koopman operator-based linear prediction dynamics of an over-actuated nonlinear robotic system. We demonstrate the derived results on a hybrid neuroprosthesis model for a trajectory tracking task wherein we show a muscle fatigue-based joint torque allocation among motor and functional electrical stimulation (FES) actuators.}, journal={IEEE CONTROL SYSTEMS LETTERS}, author={Singh, Mayank and Lambeth, Krysten and Iyer, Ashwin and Sharma, Nitin}, year={2024}, pages={145–150} }
 @article{iyer_singh_sharma_2023, title={Cooperative Control of a Hybrid Exoskeleton Using Optimal Time Varying Impedance Parameters During Stair Ascent}, ISSN={["2378-5861"]}, DOI={10.23919/ACC55779.2023.10156039}, abstractNote={Potentially, cooperative control of functional electrical stimulation (FES) and electric motors in a hybrid exoskeleton can perform stair ascent while adapting to a user’s locomotion. Towards this goal, it would be essential to determine the time varying impedance model parameters of each user while ensuring the stability of the closed loop system. While some previous studies address the stability problem when estimating time varying impedance model parameters, constraints on the parameters to their physiological values are not guaranteed. In this paper, we develop a model predictive control (MPC) based approach to prescribe physiologically constrained time varying stiffness and damping parameters for an impedance model. A terminal cost and controller for the stiffness and damping are designed to ensure the MPC problem is recursively feasible, satisfy physiological constraints, and is asymptotically stable. Another MPC-based cooperative control approach is then used to ensure that the knee joint follows the knee trajectory generated via the impedance model with optimized parameters. Simulations results show foot, knee joint, and impedance model tracking while allocating inputs between FES and motors during stair ascent and adequate foot clearance and placement.}, journal={2023 AMERICAN CONTROL CONFERENCE, ACC}, author={Iyer, Ashwin and Singh, Mayank and Sharma, Nitin}, year={2023}, pages={2739–2744} }
 @article{xue_iyer_sharma_2023, title={Koopman-based Data-driven Model Predictive Control of Limb Tremor Dynamics with Online Model Updating: A Theoretical Modeling and Simulation Approach}, ISSN={["2378-5861"]}, DOI={10.23919/ACC55779.2023.10156240}, abstractNote={Patients suffering from tremors have difficulty performing activities of daily living. The development of a model of a limb with tremors can pave the way for non-surgical tremor suppression control techniques. Nevertheless, nonlinearity and actuator saturation make it difficult to develop an accurate model and a tremor suppression control method. Towards addressing this issue, this paper describes a Koopman-based method for system identification and its application to the design of a model predictive control (MPC) scheme to suppress tremors. Since model prediction accuracy is critical to the performance of an MPC, it is essential to update the model online if the predictions are not sufficiently accurate. We propose a recursive least squares (RLS) algorithm to improve control performance with low computational complexity. Finally, for the first time, stability analysis and recursive feasibility of the Koopman-based MPC (KMPC) closed-loop updated system are presented. The proposed modeling and control approach have been validated by experimental data and simulation results.}, journal={2023 AMERICAN CONTROL CONFERENCE, ACC}, author={Xue, Xiangming and Iyer, Ashwin and Sharma, Nitin}, year={2023}, pages={2873–2878} }
 @article{xue_iyer_roque_sharma_2023, title={Nonlinear System Identification of Tremors Dynamics: A Data-driven Approximation Using Koopman Operator Theory}, ISSN={["1948-3546"]}, DOI={10.1109/NER52421.2023.10123909}, abstractNote={People who suffer from tremors have difficulty performing activities of daily living. Efforts in developing a model of a limb with tremors can pave the way for non-surgical tremor suppression techniques. However, due to the nonlinearity, developing an accurate model of tremors is challenging. This paper implements a data-driven method for approximating the Koopman operator, which is capable of presenting nonlinear dynamics in a linear framework and is promising for predicting the nonlinear system. A dynamic model of tremors is developed with ultrasound (US) image data collected from a patient with essential tremor as they grasp objects. The method is applied to predict the patient's tremor dynamics and is compared with the nonlinear Hammerstein-Wiener system identification technique.}, journal={2023 11TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING, NER}, author={Xue, Xiangming and Iyer, Ashwin and Roque, Daniel and Sharma, Nitin}, year={2023} }
 @article{sheng_iyer_sun_kim_sharma_2022, title={A Hybrid Knee Exoskeleton Using Real-Time Ultrasound-Based Muscle Fatigue Assessment}, volume={5}, ISSN={["1941-014X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85130478475&partnerID=MN8TOARS}, DOI={10.1109/TMECH.2022.3171086}, abstractNote={Ultrasound-based state assessment of the human muscle during rehabilitation and its integration into a hybrid exoskeleton comprising an functional electrical stimulation (FES) system and a powered orthosis are emerging research areas. This article presents results from the first experimental demonstration of a hybrid knee exoskeleton that uses ultrasound-derived muscle state feedback to coordinate electrical motors and FES. A significant contribution of the article is to integrate a real-time ultrasound image acquisition and processing framework into a recently derived switching-based feedback control of the hybrid knee exoskeleton. As a result, the contractility response of the quadriceps muscle to the FES input can be monitored in vivo in real-time and estimate FES-induced muscle fatigue changes in the muscle. The switched controller’s decision-making process can then use the estimated muscle fatigue to compensate or replace the FES-stimulated muscle power with an electrical motor, thus avoiding extensive stimulation of the fatigued muscle. The experimental results suggest a potential application in the rehabilitation of neurological disorders like spinal cord injuries and stroke.}, number={4}, journal={IEEE-ASME TRANSACTIONS ON MECHATRONICS}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Sheng, Zhiyu and Iyer, Ashwin and Sun, Ziyue and Kim, Kang and Sharma, Nitin}, year={2022}, month={May} }
 @article{sun_qiu_iyer_dicianno_sharma_2022, title={Continuous Switching Control of an Input-Delayed Antagonistic Muscle Pair During Functional Electrical Stimulation}, volume={6}, ISSN={["1558-0865"]}, url={https://doi.org/10.1109/TCST.2022.3178935}, DOI={10.1109/TCST.2022.3178935}, abstractNote={Existing controllers for functional electrical stimulation (FES) of upper limb muscles were initially designed to assist unilateral movements and may not be readily applicable to assist antagonistic muscle movements. Furthermore, it is yet unclear if electromechanical delays (EMDs) are present during the coactivation of muscles. In this article, a robust controller is designed to facilitate the FES of an antagonistic muscle pair during elbow flexion and extension. The controller uses a continuous switching law that maps a joint angle error to control the antagonistic muscle pair. Furthermore, the controller compensates for EMDs in the antagonistic muscle pair. A Lyapunov stability analysis yields uniformly ultimately bounded (UUB) tracking for the human limb joint. The experimental results on four participants without disabilities indicate that the controller is robust and effective in switching between antagonistic muscles. A separate set of experiments also showed that EMDs are indeed present in the coactivated muscle pair. The designed controller compensates for the EMDs and statistically improves root mean square error (RMSE) compared to a traditional linear controller with no EMD compensation. The proposed controller can be generalized to assist FES-elicited tasks that involve a weak antagonistic muscle pair.}, journal={IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Sun, Ziyue and Qiu, Tianyi and Iyer, Ashwin and Dicianno, Brad E. and Sharma, Nitin}, year={2022}, month={Jun} }
 @article{zhang_iyer_lambeth_kim_sharma_2022, title={Ultrasound Echogenicity as an Indicator of Muscle Fatigue during Functional Electrical Stimulation}, volume={22}, ISSN={["1424-8220"]}, url={https://www.mdpi.com/1424-8220/22/1/335}, DOI={10.3390/s22010335}, abstractNote={Functional electrical stimulation (FES) is a potential neurorehabilitative intervention to enable functional movements in persons with neurological conditions that cause mobility impairments. However, the quick onset of muscle fatigue during FES is a significant challenge for sustaining the desired functional movements for more extended periods. Therefore, a considerable interest still exists in the development of sensing techniques that reliably measure FES-induced muscle fatigue. This study proposes to use ultrasound (US) imaging-derived echogenicity signal as an indicator of FES-induced muscle fatigue. We hypothesized that the US-derived echogenicity signal is sensitive to FES-induced muscle fatigue under isometric and dynamic muscle contraction conditions. Eight non-disabled participants participated in the experiments, where FES electrodes were applied on their tibialis anterior (TA) muscles. During a fatigue protocol under either isometric and dynamic ankle dorsiflexion conditions, we synchronously collected the isometric dorsiflexion torque or dynamic dorsiflexion angle on the ankle joint, US echogenicity signals from TA muscle, and the applied stimulation intensity. The experimental results showed an exponential reduction in the US echogenicity relative change (ERC) as the fatigue progressed under the isometric (R2=0.891±0.081) and dynamic (R2=0.858±0.065) conditions. The experimental results also implied a strong linear relationship between US ERC and TA muscle fatigue benchmark (dorsiflexion torque or angle amplitude), with R2 values of 0.840±0.054 and 0.794±0.065 under isometric and dynamic conditions, respectively. The findings in this study indicate that the US echogenicity signal is a computationally efficient signal that strongly represents FES-induced muscle fatigue. Its potential real-time implementation to detect fatigue can facilitate an FES closed-loop controller design that considers the FES-induced muscle fatigue.}, number={1}, journal={SENSORS}, author={Zhang, Qiang and Iyer, Ashwin and Lambeth, Krysten and Kim, Kang and Sharma, Nitin}, year={2022}, month={Jan} }
 @article{zhang_lambeth_iyer_sun_sharma_2022, title={Ultrasound Imaging-Based Closed-Loop Control of Functional Electrical Stimulation for Drop Foot Correction}, volume={9}, ISSN={["1558-0865"]}, url={https://doi.org/10.1109/TCST.2022.3207999}, DOI={10.1109/TCST.2022.3207999}, abstractNote={Open- or closed-loop functional electrical stimulation (FES) has been widely investigated to treat drop foot syndrome, which is typically caused by weakness or paralysis of ankle dorsiflexors. However, conventional closed-loop FES control mainly uses kinematic feedback, which does not directly capture time-varying changes in muscle activation. In this study, we explored the use of ultrasound (US) echogenicity as an indicator of FES-evoked muscle activation and hypothesized that including US-derived muscle activation, in addition to kinematic feedback, would improve the closed-loop FES control performance compared to the closed-loop control that relies only on the kinematic feedback. A sampled-data observer (SDO) was derived to continuously estimate FES-evoked muscle activations from low-sampled US echogenicity signals. In addition, a dynamic surface controller (DSC) and a delay compensation (DC) term were incorporated with the SDO, denoted as the US-based DSC-DC, to drive the actual ankle dorsiflexion trajectory to the desired profile. The trajectory tracking error convergence of the closed-loop system was proven to be uniformly ultimately bounded based on the Lyapunov–Krasovskii stability analysis. The US-based DSC-DC controller was validated on five participants with no disabilities to control their ankle dorsiflexion during walking on a treadmill. The US-based DSC-DC controller significantly reduced the root-mean-square error of the ankle joint trajectory tracking by 46.52% ± 7.99% ( $p < 0.001$ ) compared to the traditional DSC-DC controller with only kinematic feedback but no US measurements. The results also verified the disturbance rejection performance of the US-based DSC-DC controller when a plantarflexion disturbance was added. Our control design, for the first time, provides a methodology to integrate US in an FES control framework, which will likely benefit persons with drop foot and those with other mobility disorders.}, journal={IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY}, author={Zhang, Qiang and Lambeth, Krysten and Iyer, Ashwin and Sun, Ziyue and Sharma, Nitin}, year={2022}, month={Sep} }
 @article{zhang_iyer_sun_kim_sharma_2021, title={A Dual-Modal Approach Using Electromyography and Sonomyography Improves Prediction of Dynamic Ankle Movement: A Case Study}, volume={29}, ISSN={["1558-0210"]}, url={https://doi.org/10.1109/TNSRE.2021.3106900}, DOI={10.1109/TNSRE.2021.3106900}, abstractNote={For decades, surface electromyography (sEMG) has been a popular non-invasive bio-sensing technology for predicting human joint motion. However, cross-talk, interference from adjacent muscles, and its inability to measure deeply located muscles limit its performance in predicting joint motion. Recently, ultrasound (US) imaging has been proposed as an alternative non-invasive technology to predict joint movement due to its high signal-to-noise ratio, direct visualization of targeted tissue, and ability to access deep-seated muscles. This paper proposes a dual-modal approach that combines US imaging and sEMG for predicting volitional dynamic ankle dorsiflexion movement. Three feature sets: 1) a uni-modal set with four sEMG features, 2) a uni-modal set with four US imaging features, and 3) a dual-modal set with four dominant sEMG and US imaging features, together with measured ankle dorsiflexion angles, were used to train multiple machine learning regression models. The experimental results from a seated posture and five walking trials at different speeds, ranging from 0.50 m/s to 1.50 m/s, showed that the dual-modal set significantly reduced the prediction root mean square errors (RMSEs). Compared to the uni-modal sEMG feature set, the dual-modal set reduced RMSEs by up to 47.84% for the seated posture and up to 77.72% for the walking trials. Similarly, when compared to the US imaging feature set, the dual-modal set reduced RMSEs by up to 53.95% for the seated posture and up to 58.39% for the walking trials. The findings show that potentially the dual-modal sensing approach can be used as a superior sensing modality to predict human intent of a continuous motion and implemented for volitional control of clinical rehabilitative and assistive devices.}, journal={IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Zhang, Qiang and Iyer, Ashwin and Sun, Ziyue and Kim, Kang and Sharma, Nitin}, year={2021}, pages={1944–1954} }
 @article{zhang_iyer_kim_sharma_2021, title={Evaluation of Non-Invasive Ankle Joint Effort Prediction Methods for Use in Neurorehabilitation Using Electromyography and Ultrasound Imaging}, volume={68}, ISSN={["1558-2531"]}, url={https://doi.org/10.1109/TBME.2020.3014861}, DOI={10.1109/TBME.2020.3014861}, abstractNote={Objective: Reliable measurement of voluntary human effort is essential for effective and safe interaction between the wearer and an assistive robot. Existing voluntary effort prediction methods that use surface electromyography (sEMG) are susceptible to prediction inaccuracies due to non-selectivity in measuring muscle responses. This technical challenge motivates an investigation into alternative non-invasive effort prediction methods that directly visualize the muscle response and improve effort prediction accuracy. The paper is a comparative study of ultrasound imaging (US)-derived neuromuscular signals and sEMG signals for their use in predicting isometric ankle dorsiflexion moment. Furthermore, the study evaluates the prediction accuracy of model-based and model-free voluntary effort prediction approaches that use these signals. Methods: The study evaluates sEMG signals and three US imaging-derived signals: pennation angle, muscle fascicle length, and echogenicity and three voluntary effort prediction methods: linear regression (LR), feedforward neural network (FFNN), and Hill-type neuromuscular model (HNM). Results: In all the prediction methods, pennation angle and fascicle length significantly improve the prediction accuracy of dorsiflexion moment, when compared to echogenicity. Also, compared to LR, both FFNN and HNM improve dorsiflexion moment prediction accuracy. Conclusion: The findings indicate FFNN or HNM approach and using pennation angle or fascicle length predict human ankle movement intent with higher accuracy. Significance: The accurate ankle effort prediction will pave the path to safe and reliable robotic assistance in patients with drop foot.}, number={3}, journal={IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Zhang, Qiang and Iyer, Ashwin and Kim, Kang and Sharma, Nitin}, year={2021}, month={Mar}, pages={1044–1055} }
 @inproceedings{zhang_iyer_lambeth_kim_sharma_2021, title={Ultrasound Echogenicity-based Assessment of Muscle Fatigue During Functional Electrical Stimulation}, ISSN={["1558-4615"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85122094226&partnerID=MN8TOARS}, DOI={10.1109/EMBC46164.2021.9630325}, abstractNote={The rapid onset of muscle fatigue during functional electrical stimulation (FES) is a major challenge when attempting to perform long-term periodic tasks such as walking. Surface electromyography (sEMG) is frequently used to detect muscle fatigue for both volitional and FES-evoked muscle contraction. However, sEMG contamination from both FES stimulation artifacts and residual M-wave signals requires sophisticated processing to get clean signals and evaluate the muscle fatigue level. The objective of this paper is to investigate the feasibility of computationally efficient ultrasound (US) echogenicity as a candidate indicator of FES-induced muscle fatigue. We conducted isometric and dynamic ankle dorsiflexion experiments with electrically stimulated tibialis anterior (TA) muscle on three human participants. During a fatigue protocol, we synchronously recorded isometric dorsiflexion force, dynamic dorsiflexion angle, US images, and stimulation intensity. The temporal US echogenicity from US images was calculated based on a gray-scaled analysis to assess the decrease in dorsiflexion force or motion range due to FES-induced TA muscle fatigue. The results showed a monotonic reduction in US echogenicity change along with the fatigue progression for both isometric (R2 =0.870±0.026) and dynamic (R2 =0.803±0.048) ankle dorsiflexion. These results implied a strong linear relationship between US echogenicity and TA muscle fatigue level. The findings indicate that US echogenicity may be a promising computationally efficient indicator for assessing FES-induced muscle fatigue and may aid in the design of muscle-in-the-loop FES controllers that consider the onset of muscle fatigue.}, booktitle={2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)}, author={Zhang, Q. and Iyer, A. and Lambeth, K. and Kim, K. and Sharma, N.}, year={2021}, month={Nov}, pages={5948–5952} }