@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{zhang_lambeth_sun_dodson_bao_sharma_2023, title={Evaluation of a Fused Sonomyography and Electromyography-Based Control on a Cable-Driven Ankle Exoskeleton}, volume={2}, ISSN={["1941-0468"]}, url={https://doi.org/10.1109/TRO.2023.3236958}, DOI={10.1109/TRO.2023.3236958}, abstractNote={This article presents an assist-as-needed (AAN) control framework for exoskeleton assistance based on human volitional effort prediction via a Hill-type neuromuscular model. A sequential processing algorithm-based multirate observer is applied to continuously estimate muscle activation levels by fusing surface electromyography (sEMG) and ultrasound (US) echogenicity signals from the ankle muscles. An adaptive impedance controller manipulates the exoskeleton's impedance for a more natural behavior by following a desired intrinsic impedance model. Two neural networks provide robustness to uncertainties in the overall ankle joint-exoskeleton model and the prediction error in the volitional ankle joint torque. A rigorous Lyapunov-based stability analysis proves that the AAN control framework achieves uniformly ultimately bounded tracking for the overall system. Experimental studies on five participants with no neurological disabilities walking on a treadmill validate the effectiveness of the designed ankle exoskeleton and the proposed AAN approach. Results illustrate that the AAN control approach with fused sEMG and US echogenicity signals maintained a higher human volitional effort prediction accuracy, less ankle joint trajectory tracking error, and less robotic assistance torque than the AAN approach with the sEMG-based volitional effort prediction alone. The findings support our hypotheses that the proposed controller increases human motion intent prediction accuracy, improves the exoskeleton's control performance, and boosts voluntary participation from human subjects. The new framework potentially paves a foundation for using multimodal biological signals to control rehabilitative or assistive robots.}, journal={IEEE TRANSACTIONS ON ROBOTICS}, author={Zhang, Qiang and Lambeth, Krysten and Sun, Ziyue and Dodson, Albert and Bao, Xuefeng and Sharma, Nitin}, year={2023}, month={Feb} }
 @article{lambeth_singh_sharma_2023, title={Robust Control Barrier Functions for Safety Using a Hybrid Neuroprosthesis}, ISSN={["2378-5861"]}, DOI={10.23919/ACC55779.2023.10155862}, abstractNote={Many lower-limb hybrid neuroprostheses lack powered ankle assistance and thus cannot compensate for functional electrical stimulation-induced muscle fatigue at the ankle joint. The lack of a powered ankle joint poses a safety issue for users with foot drop who cannot volitionally clear the ground during walking. We propose zeroing control barrier functions (ZCBFs) that guarantee safe foot clearance and fatigue mitigation, provided that the trajectory begins within the prescribed safety region. We employ a backstepping-based model predictive controller (MPC) to account for activation dynamics, and we formulate a constraint to ensure the ZCBF is robust to modeling uncertainty and disturbance. Simulations show the superior performance of the proposed robust MPC-ZCBF scheme for achieving foot clearance compared to traditional ZCBFs and Euclidean safety constraints.}, journal={2023 AMERICAN CONTROL CONFERENCE, ACC}, author={Lambeth, Krysten and Singh, Mayank and Sharma, Nitin}, year={2023}, pages={54–59} }
 @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} }
 @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} }