@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{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} }