@article{bao_zhang_fragnito_wang_sharma_2023, title={A clustering-based method for estimating pennation angle from B-mode ultrasound images}, url={https://doi.org/10.1017/wtc.2022.30}, DOI={10.1017/wtc.2022.30}, abstractNote={Abstract}, journal={Wearable Technologies}, author={Bao, Xuefeng and Zhang, Qiang and Fragnito, Natalie and Wang, Jian and Sharma, Nitin}, year={2023} }
 @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{zhang_fragnito_bao_sharma_2022, title={A deep learning method to predict ankle joint moment during walking at different speeds with ultrasound imaging: A framework for assistive devices control}, url={https://doi.org/10.1017/wtc.2022.18}, DOI={10.1017/wtc.2022.18}, abstractNote={Abstract}, journal={Wearable Technologies}, author={Zhang, Qiang and Fragnito, Natalie and Bao, Xuefeng and Sharma, Nitin}, year={2022} }
 @article{molazadeh_zhang_bao_sharma_2022, title={An Iterative Learning Controller for a Switched Cooperative Allocation Strategy During Sit-to-Stand Tasks with a Hybrid Exoskeleton}, volume={30}, ISSN={["1558-0865"]}, url={https://doi.org/10.1109/TCST.2021.3089885}, DOI={10.1109/TCST.2021.3089885}, abstractNote={A hybrid exoskeleton that combines functional electrical stimulation (FES) and a powered exoskeleton is an emerging technology for assisting people with mobility disorders. The cooperative use of FES and the exoskeleton allows active muscle contractions through FES while robustifying torque generation to reduce FES-induced muscle fatigue. In this article, a switched distribution of allocation ratios between FES and electric motors in a closed-loop adaptive control design is explored for the first time. The new controller uses an iterative learning neural network (NN)-based control law to compensate for structured and unstructured parametric uncertainties in the hybrid exoskeleton model. A discrete Lyapunov-like stability analysis that uses a common energy function proves asymptotic stability for the switched system with iterative learning update laws. Five human participants, including a person with complete spinal cord injury, performed sit-to-stand tasks with the new controller. The experimental results showed that the synthesized controller, in a few iterations, reduced the root mean square error between desired positions and actual positions of the knee and hip joints by 46.20% and 53.34%, respectively. The sit-to-stand experimental results also show that the proposed NN-based iterative learning control (NNILC) approach can recover the asymptotically trajectory tracking performance despite the switching of allocation levels between FES and electric motor. Compared to a proportional-derivative controller and traditional iterative learning control, the findings showed that the new controller can potentially simplify the clinical implementation of the hybrid exoskeleton with minimal parameters tuning.}, number={3}, journal={IEEE Transactions on Control Systems Technology}, publisher={IEEE}, author={Molazadeh, V. and Zhang, Q. and Bao, X. and Sharma, N.}, year={2022}, month={May}, pages={1021–1036} }
 @article{zhang_fragnito_franz_sharma_2022, title={Fused Ultrasound And Electromyography-Driven Neuromuscular Model To Improve Plantarflexion Moment Prediction Across Walking Speeds}, url={https://doi.org/10.21203/rs.3.rs-1136552/v1}, DOI={10.21203/rs.3.rs-1136552/v1}, abstractNote={Abstract}, author={Zhang, Qiang and Fragnito, Natalie and Franz, Jason R. and Sharma, Nitin}, year={2022}, month={Jan} }
 @article{zhang_fragnito_franz_sharma_2022, title={Fused Ultrasound and Electromyography-driven Neuromuscular Model to Improve Plantarflexion Moment Prediction across Walking Speeds}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85132172249&partnerID=MN8TOARS}, DOI={10.21203/rs.3.rs-1136552}, journal={ResearchSquare}, author={Zhang, Q. and Fragnito, N. and Franz, J.R. and Sharma, N.}, year={2022} }
 @article{zhang_fragnito_franz_sharma_2022, title={Fused ultrasound and electromyography-driven neuromuscular model to improve plantarflexion moment prediction across walking speeds}, volume={19}, url={https://doi.org/10.1186/s12984-022-01061-z}, DOI={10.1186/s12984-022-01061-z}, abstractNote={Abstract}, number={1}, journal={Journal of NeuroEngineering and Rehabilitation}, author={Zhang, Qiang and Fragnito, Natalie and Franz, Jason R. and Sharma, Nitin}, year={2022}, month={Aug} }
 @article{zhang_nalam_tu_li_si_lewek_huang_2022, title={Imposing Healthy Hip Motion Pattern and Range by Exoskeleton Control for Individualized Assistance}, volume={7}, url={https://doi.org/10.1109/LRA.2022.3196105}, DOI={10.1109/LRA.2022.3196105}, abstractNote={Powered exoskeletons are promising devices to improve the walking patterns of people with neurological impairments. Providing personalized external assistance though is challenging due to uncertainties and the time-varying nature of human-robot interaction. Recently, human-in-the-loop (HIL) optimization has been investigated for providing assistance to minimize energetic expenditure, usually quantified by metabolic cost. However, this full-body global effect evaluation may not directly reflect the local functions of the targeted joint(s). This makes it difficult to assess the direct effect when robotic assistance is provided. In addition, the HIL optimization method usually does not take into account local joint trajectories, a consideration that is important in imposing healthy joint movements and gait patterns for individuals with lower limb motor deficits. In this paper, we propose a model-free reinforcement learning (RL)-based control framework to achieve a normative range of motion and gait pattern of the hip joint during walking. Our RL-based control provides personalized assistance torque profile by heuristically manipulating three control parameters for hip flexion and extension, respectively, during walking. A least square policy iteration was devised to optimize a cost function associated with control efforts and hip joint trajectory errors by tuning the control parameters. To evaluate the performance of the design approach, a compression sleeve was used to constrain the hip joint of unimpaired human participants to simulate motor deficits. The proposed RL control successfully achieved the desired goal of enlarging the hip joint's range of motion in three participants walking on a treadmill.}, number={4}, journal={IEEE Robotics and Automation Letters}, author={Zhang, Qiang and Nalam, Varun and Tu, Xikai and Li, Minhan and Si, Jennie and Lewek, Michael D. and Huang, He Helen}, year={2022}, month={Oct}, pages={11126–11133} }
 @article{zhang_clark_franz_sharma_2022, title={Personalized fusion of ultrasound and electromyography-derived neuromuscular features increases prediction accuracy of ankle moment during plantarflexion}, volume={71}, ISSN={["1746-8108"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85114124714&partnerID=MN8TOARS}, DOI={10.1016/j.bspc.2021.103100}, abstractNote={Compared to mechanical signals that are used for estimating human limb motion intention, non-invasive surface electromyography (sEMG) is a preferred signal in human-robotic systems. However, noise interference, crosstalk from adjacent muscle groups, and an inability to measure deeper muscle tissues are disadvantageous to sEMG’s reliable use. In this work, we hypothesize that a fusion between sEMG and in vivo ultrasound (US) imaging will result in more accurate detection of ankle movement intention. Nine young able-bodied participants were included to volitionally perform isometric plantarflexion tasks with different fixed-end ankle postures, while the sEMG and US imaging data of plantarflexors were synchronously collected. We created three dominant feature sets, sole sEMG feature set, sole US feature set, and sEMG-US feature fusion set, to calibrate and validate a support vector machine regression model (SVR) and a feedforward neural network model (FFNN) with labeled net moment measurements. The results showed that, compared to the sole sEMG feature set, the sEMG-US fusion set reduced the average net moment prediction error by 35.7% (p < 0.05), when using SVR, and by 21.5% (p < 0.05), when using FFNN. In SVR, the sole US feature set reduced the prediction error by 24.9% (p < 0.05) when compared to the sole sEMG feature set. In FFNN, the sEMG-US fusion set reduced the prediction error by 28.2% (p < 0.05) when compared to the sole US feature set. These findings indicate that the combination of sEMG signals and US imaging is a superior sensing modality for predicting human plantarflexion intention and can enable future clinical rehabilitation devices.}, journal={BIOMEDICAL SIGNAL PROCESSING AND CONTROL}, author={Zhang, Qiang and Clark, William H. and Franz, Jason R. and Sharma, Nitin}, year={2022}, month={Jan} }
 @article{wang_zhang_xiao_2022, title={Trajectory Tracking Control of the Bionic Joint Actuated by Pneumatic Artificial Muscle Based on Robust Modeling}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85136531502&partnerID=MN8TOARS}, DOI={10.48550/arXiv.2208.07157}, abstractNote={To simply and effectively realize the trajectory tracking control of a bionic joint actuated by a single pneumatic artificial muscle (PAM), a cascaded control strategy is proposed based on the robust modeling method. Firstly, the relationship between the input voltage of the proportional directional control valve and the inner driving pressure of PAM is expressed as a nonlinear model analytically. Secondly, the nonlinear relationship between the driving pressure input of PAM and the angular position output of the bionic joint is described as a second-order linear time-invariant model (LTI) accompanied by parametric perturbations, equivalently, and then the parameters of the model are identified by the robust modeling method. Then, a hybrid model is established based on the two models (the nonlinear model and the LTI model) and corresponding to it, a cascaded controller is developed, the outer loop of which is an H-infinite controller for the angular position tracking designed by loop-shaping design procedure (LSDP) and the inner loop is a nonlinear controller based on the feedback linearization theory for the PAM driving pressure control. Finally, the experiment is accomplished within the joint rotation range of 90 degrees and with the working frequency upper bound of 1.25 rad/s. And the joint with the developed cascaded controller tracks given reference trajectories with steady-state errors smaller than 2%. Results show that the trajectory tracking control of a highly nonlinear system is highly efficient using the proposed strategy in the case of relatively low work frequency.}, journal={arXiv}, author={Wang, Y. and Zhang, Q. and Xiao, X.-H.}, year={2022} }
 @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} }
 @inbook{zhang_iyer_sharma_2022, title={Ultrasound-Based Sensing and Control of Functional Electrical Stimulation for Ankle Joint Dorsiflexion: Preliminary Study}, volume={27}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85109560445&partnerID=MN8TOARS}, DOI={10.1007/978-3-030-69547-7_50}, abstractNote={Functional electrical stimulation (FES) is a potential technique for reanimating paralyzed muscles post neurological injury/disease. Several technical challenges, including the difficulty in measuring FES-induced muscle activation and muscle fatigue, and compensating for the electromechanical delay (EMD) during muscle force generation, inhibit its satisfactory control performance. In this paper, an ultrasound (US) imaging approach is proposed to observe muscle activation and fatigue levels during FES-elicited ankle dorsiflexors. Due to the low sampling rate of the US imaging-derived signal, a sampled-data observer (SDO) is designed to continuously estimate the muscle activation and fatigue based on their continuous dynamics. The SDO is combined with a delay compensation term to address the ankle dorsiflexion trajectory tracking problem with a known input delay. Experimental results on an able-bodied participant show the effectiveness of the proposed control method, and the superior tracking performance compared to a traditional control method, where the muscle activation and fatigue are computed from an off-line identified model.}, booktitle={Biosystems and Biorobotics}, author={Zhang, Q. and Iyer, A. and Sharma, N.}, year={2022}, pages={307–311} }
 @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} }
 @inproceedings{sun_bao_zhang_lambeth_sharma_2021, title={A Tube-based Model Predictive Control Method for Joint Angle Tracking with Functional Electrical Stimulation and An Electric Motor Assist}, volume={2021-May}, ISBN={9781665441971}, url={http://dx.doi.org/10.23919/acc50511.2021.9483084}, DOI={10.23919/ACC50511.2021.9483084}, abstractNote={During functional electrical stimulation (FES), muscle force saturation and a user's tolerance levels of stimulation intensity limit a controller's ability to deliver the desired amount of stimulation, which, if unaddressed, degrade the performance of high-gain feedback control strategies. Additionally, these strategies may overstimulate the muscles, which further contribute to the rapid onset of muscle fatigue. Cooperative control of FES with an electric motor assist may allow stimulation levels within the imposed limits, reduce overall stimulation duty cycle, and compensate for the muscle fatigue. Model predictive controller (MPC) is one such optimal control strategy to achieve these control objectives of the combined hybrid system. However, the traditional MPC method for the hybrid system requires exact model knowledge of the dynamic system, i.e., cannot handle modeling uncertainties, and the recursive feasibility has been shown only for limb regulation problems. So far, extending the current results to a limb tracking problem has been challenging. In this paper, a novel tube-based MPC method for tracking control of a human limb angle by cooperatively using FES and electric motor inputs is derived. A feedback controller for the electrical motor assist is designed such that it reduces the error between the nominal MPC and the output of the actual hybrid system. Further, a terminal controller and terminal constraint region are derived to show the recursive feasibility of the robust MPC scheme. Simulation results were performed on a single degree of freedom knee extension model. The results show robust performance despite modeling uncertainties.}, booktitle={2021 American Control Conference (ACC)}, publisher={IEEE}, author={Sun, Ziyue and Bao, Xuefeng and Zhang, Qiang and Lambeth, Krysten and Sharma, Nitin}, year={2021}, month={May}, pages={1390–1395} }
 @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_fragnito_myers_sharma_2021, title={Plantarflexion Moment Prediction during the Walking Stance Phase with an sEMG-Ultrasound Imaging-Driven Model}, volume={2021-January}, ISSN={["1558-4615"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85122533499&partnerID=MN8TOARS}, DOI={10.1109/EMBC46164.2021.9630046}, abstractNote={Many rehabilitative exoskeletons use non-invasive surface electromyography (sEMG) to measure human volitional intent. However, signals from adjacent muscle groups interfere with sEMG measurements. Further, the inability to measure sEMG signals from deeply located muscles may not accurately measure the volitional intent. In this work, we combined sEMG and ultrasound (US) imaging-derived signals to improve the prediction accuracy of voluntary ankle effort. We used a multivariate linear model (MLM) that combines sEMG and US signals for ankle joint net plantarflexion (PF) moment prediction during the walking stance phase. We hypothesized that the proposed sEMG-US imaging-driven MLM would result in more accurate net PF moment prediction than sEMG-driven and US imaging-driven MLMs. Synchronous measurements including reflective makers coordinates, ground reaction forces, sEMG signals of lateral/medial gastrocnemius (LGS/MGS), and soleus (SOL) muscles, and US imaging of LGS and SOL muscles were collected from five able-bodied participants walking on a treadmill at multiple speeds. The ankle joint net PF moment benchmark was calculated based on inverse dynamics, while the net PF moment prediction was determined by the sEMG-US imaging-driven, sEMG-driven, and US imaging-driven MLMs. The findings show that the sEMG-US imaging-driven MLM can significantly improve the prediction of net PF moment during the walking stance phase at multiple speeds. Potentially, the proposed sEMG-US imaging-driven MLM can be used as a superior joint motion intent model in advanced and intelligent control strategies for rehabilitative exoskeletons.}, booktitle={2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)}, author={Zhang, Q. and Fragnito, N. and Myers, A. and Sharma, N.}, year={2021}, month={Nov}, pages={6267–6272} }
 @article{molazadeh_zhang_bao_dicianno_sharma_2021, title={Shared Control of a Powered Exoskeleton and Functional Electrical Stimulation Using Iterative Learning}, volume={8}, ISSN={["2296-9144"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85119421007&partnerID=MN8TOARS}, DOI={10.3389/frobt.2021.711388}, abstractNote={A hybrid exoskeleton comprising a powered exoskeleton and functional electrical stimulation (FES) is a promising technology for restoration of standing and walking functions after a neurological injury. Its shared control remains challenging due to the need to optimally distribute joint torques among FES and the powered exoskeleton while compensating for the FES-induced muscle fatigue and ensuring performance despite highly nonlinear and uncertain skeletal muscle behavior. This study develops a bi-level hierarchical control design for shared control of a powered exoskeleton and FES to overcome these challenges. A higher-level neural network–based iterative learning controller (NNILC) is derived to generate torques needed to drive the hybrid system. Then, a low-level model predictive control (MPC)-based allocation strategy optimally distributes the torque contributions between FES and the exoskeleton’s knee motors based on the muscle fatigue and recovery characteristics of a participant’s quadriceps muscles. A Lyapunov-like stability analysis proves global asymptotic tracking of state-dependent desired joint trajectories. The experimental results on four non-disabled participants validate the effectiveness of the proposed NNILC-MPC framework. The root mean square error (RMSE) of the knee joint and the hip joint was reduced by 71.96 and 74.57%, respectively, in the fourth iteration compared to the RMSE in the 1st sit-to-stand iteration.}, journal={FRONTIERS IN ROBOTICS AND AI}, author={Molazadeh, Vahidreza and Zhang, Qiang and Bao, Xuefeng and Dicianno, Brad E. and Sharma, Nitin}, year={2021}, month={Nov} }
 @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} }
 @inproceedings{iyer_sheng_zhang_kim_sharma_2020, place={Hoboken, NJ}, title={Analysis of Tremor During Grasp Using Ultrasound Imaging: Preliminary Study}, volume={2020-November}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85095593827&partnerID=MN8TOARS}, DOI={10.1109/BioRob49111.2020.9224446}, abstractNote={This paper investigates the use of ultrasound imaging to characterize tremor during a grasping motion. Ultrasound images were collected from three human participants including an able-bodied participant, a patient with Parkinson’s disease, and a patient with essential tremor. Each human participant was instructed to grasp and hold objects with three different masses in a vertical upright position with an ultrasound probe strapped to their forearm while seated. The images were processed using an ultrasound speckle tracking algorithm to measure muscle strain during the grasping and holding motion. Analysis of the computed strain values showed marked differences in the strain peaks and frequencies between able-bodied participant and the patients with tremor. The detected frequencies depict how the strain measurement changes during the grasping and holding motion. The frequency for tremor participants fall within accepted frequency ranges for Parkinson’s Disease and Essential Tremor, and thus can be representative of the actual tremor frequency.}, booktitle={Proceedings of the 8th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics}, publisher={IEEE}, author={Iyer, A. and Sheng, Z. and Zhang, Q. and Kim, K. and Sharma, N.}, year={2020}, pages={533–538} }
 @article{zhang_sun_qian_xiao_guo_2020, title={Modeling and Control of a Cable-Driven Rotary Series Elastic Actuator for an Upper Limb Rehabilitation Robot}, volume={14}, ISSN={["1662-5218"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85081674856&partnerID=MN8TOARS}, DOI={10.3389/fnbot.2020.00013}, abstractNote={This paper focuses on the design, modeling, and control of a novel remote actuation, including a compact rotary series elastic actuator (SEA) and Bowden cable. This kind of remote actuation is used for an upper limb rehabilitation robot (ULRR) with four powered degrees of freedom (DOFs). The SEA mainly consists of a DC motor with planetary gearheads, inner/outer sleeves, and eight linearly translational springs. The key innovations include (1) an encoder for direct spring displacement measurement, which can be used to calculate the output torque of SEA equivalently, (2) the embedded springs can absorb the negative impact of backlash on SEA control performance, (3) and the Bowden cable enables long-distance actuation and reduces the bulky structure on the robotic joint. In modeling of this actuation, the SEA's stiffness coefficient, the dynamics of the SEA, and the force transmission of the Bowden cable are considered for computing the inputs on each powered joint of the robot. Then, both torque and impedance controllers consisting of proportional-derivative (PD) feedback, disturbance observer (DOB), and feedforward compensation terms are developed. Simulation and experimental results verify the performance of these controllers. The preliminary results show that this new kind of actuation can not only implement stable and friendly actuation over a long distance but also be customized to meet the requirements of other robotic system design.}, journal={FRONTIERS IN NEUROROBOTICS}, author={Zhang, Qiang and Sun, Dingyang and Qian, Wei and Xiao, Xiaohui and Guo, Zhao}, year={2020}, month={Feb} }
 @article{zhang_kim_sharma_2020, title={Prediction of Ankle Dorsiflexion Moment by Combined Ultrasound Sonography and Electromyography}, volume={28}, ISSN={["1558-0210"]}, url={https://doi.org/10.1109/TNSRE.2019.2953588}, DOI={10.1109/TNSRE.2019.2953588}, abstractNote={To provide an effective and safe therapy to persons with neurological impairments, accurate determination of their residual volitional ability is required. However, accurate measurement of the volitional ability, through non-invasive means (e.g., electromyography), is challenging due to signal interference from neighboring muscles or stimulation artifacts caused by functional electrical stimulation (FES). In this work, a new model-based intention detection method that combines signals from both surface electromyography (sEMG) and ultrasound (US) sonography to predict isometric volitional ankle dorsiflexion moment is proposed. The work is motivated by the fact that the US-derived signals, unlike sEMG, provide direct visualization of the muscle activity, and hence may enhance the prediction accuracy of the volitional ability, when combined with sEMG. The weighted summation of sEMG and US imaging signals, measured on the tibialis anterior muscle, is utilized as an input to a modified Hill-type neuromusculoskeletal model that predicts the ankle dorsiflexion moment. The effectiveness of the proposed model-based moment prediction method is validated by comparing the predicted and the measured ankle joint moments. The new modeling method has a better prediction accuracy compared to a prediction model that uses sole sEMG or sole US sonography. This finding provides a more accurate approach to detect movement intent in the lower limbs. The approach can be potentially beneficial for the development of US sonography-based robotic or FES-assisted rehabilitation devices.}, number={1}, journal={IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Zhang, Qiang and Kim, Kang and Sharma, Nitin}, year={2020}, month={Jan}, pages={318–327} }
 @inproceedings{zhang_iyer_sun_dodson_sharma_2020, title={Sampled-Data Observer Based Dynamic Surface Control of Delayed Neuromuscular Functional Electrical Stimulation}, volume={1}, ISBN={9780791884270}, url={http://dx.doi.org/10.1115/dscc2020-3225}, DOI={10.1115/DSCC2020-3225}, abstractNote={Abstract}, number={DSCC2020-3225DSCC2020-3225}, booktitle={Volume 1: Adaptive/Intelligent Sys. Control; Driver Assistance/Autonomous Tech.; Control Design Methods; Nonlinear Control; Robotics; Assistive/Rehabilitation Devices; Biomedical/Neural Systems; Building Energy Systems; Connected Vehicle Systems; Control/Estimation of Energy Systems; Control Apps.; Smart Buildings/Microgrids; Education; Human-Robot Systems; Soft Mechatronics/Robotic Components/Systems; Energy/Power Systems; Energy Storage; Estimation/Identification; Vehicle Efficiency/Emissions}, publisher={American Society of Mechanical Engineers}, author={Zhang, Qiang and Iyer, Ashwin and Sun, Ziyue and Dodson, Albert and Sharma, Nitin}, year={2020}, month={Oct} }
 @inproceedings{zhang_iyer_kim_sharma_2020, title={Volitional Contractility Assessment of Plantar Flexors by Using Non-invasive Neuromuscular Measurements}, volume={2020-November}, ISBN={9781728159072}, url={http://dx.doi.org/10.1109/biorob49111.2020.9224298}, DOI={10.1109/BioRob49111.2020.9224298}, abstractNote={This paper investigates an ultrasound (US) imaging-based methodology to assess the contraction levels of plantar flexors quantitatively. Echogenicity derived from US imaging at different anatomical depths, including both lateral gastrocnemius (LGS) and soleus (SOL) muscles, is used for the prediction of the volitional isometric plantar flexion moment. Synchronous measurements, including a plantar flexion torque signal, a surface electromyography (sEMG) signal, and US imaging of both LGS and SOL muscles, are collected. Four feature sets, including sole sEMG, sole LGS echogenicity, sole SOL echogenicity, and their fusion, are used to train a Gaussian process regression (GPR) model and predict plantar flexion torque. The experimental results on four non-disabled participants show that the torque prediction accuracy is improved significantly by using the LGS or SOL echogenicity signal than using the sEMG signal. However, there is no significant improvement by using the fused feature compared to sole LGS or SOL echogenicity. The findings imply that using US imagingderived signals improves the accuracy of predicting volitional effort on human plantar flexors. Potentially, US imaging can be used as a new sensing modality to measure or predict human lower limb motion intent in clinical rehabilitation devices.}, booktitle={2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)}, publisher={IEEE}, author={Zhang, Qiang and Iyer, Ashwin and Kim, Kang and Sharma, Nitin}, year={2020}, month={Nov}, pages={515–520} }
 @inproceedings{zhang_sheng_moore-clingenpeel_kim_sharma_2019, place={Hoboken, NJ}, title={Ankle Dorsiflexion Strength Monitoring by Combining Sonomyography and Electromyography}, volume={2019-June}, ISBN={9781728127552}, url={http://dx.doi.org/10.1109/icorr.2019.8779530}, DOI={10.1109/ICORR.2019.8779530}, abstractNote={Ankle dorsiflexion produced by Tibialis Anterior (TA) muscle contraction plays a significant role during human walking and standing balance. The weakened function or dysfunction of the TA muscle often impedes activities of daily living (ADL). Powered ankle exoskeleton is a prevalent technique to treat this pathology, and its intelligent and effective behaviors depend on human intention detection. A TA muscle contraction strength monitor is proposed to evaluate the weakness of the ankle dorsiflexion. The new method combines surface electromyography (sEMG) signals and sonomyography signals to estimate ankle torque during a voluntary isometric ankle dorsiflexion. Changes in the pennation angle (PA) are derived from the sonomyography signals. The results demonstrate strong correlations among the sonomyography-derived PA, the sEMG signal, and the measured TA muscle contraction force. Especially, the TA muscle strength monitor approximates the TA muscle strength measurement via a weighted summation of the sEMG signal and the PA signal. The new method shows an improved linear correlation with the muscle strength, compared to the correlations between the muscle strength and sole sEMG signal or sole PA signal, where the R-squared values are improved by 4.21 % and 1.99 %, respectively.}, booktitle={2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)}, publisher={IEEE}, author={Zhang, Qiang and Sheng, Zhiyu and Moore-Clingenpeel, Frank and Kim, Kang and Sharma, Nitin}, year={2019}, month={Jun}, pages={240–245} }
 @inproceedings{molazadeh_zhang_bao_sharma_2019, title={Neural-network based iterative learning control of a hybrid exoskeleton with an MPC allocation strategy}, volume={1}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85076504457&partnerID=MN8TOARS}, DOI={10.1115/DSCC2019-9191}, abstractNote={In this paper, a novel neural network based iterative learning controller for a hybrid exoskeleton is presented. The control allocation between functional electrical stimulation and knee electric motors uses a model predictive control strategy. Further to address modeling uncertainties, the controller identifies the system dynamics and input gain matrix with neural networks in an iterative fashion. Virtual constraints are employed so that the system can use a time invariant manifold to determine desired joint angles. Simulation results show that the controller stabilizes the hybrid system for sitting to standing and standing to sitting scenarios.}, number={DSCC2019-9191}, booktitle={ASME 2019 Dynamic Systems and Control Conference, DSCC 2019}, author={Molazadeh, V. and Zhang, Q. and Bao, X. and Sharma, Nitin}, year={2019} }
 @inproceedings{zhang_sheng_kim_sharma_2019, title={Observer Design for a Nonlinear Neuromuscular System with Multi-rate Sampled and Delayed Output Measurements}, volume={2019-July}, ISBN={9781538679265}, url={http://dx.doi.org/10.23919/acc.2019.8814473}, DOI={10.23919/acc.2019.8814473}, abstractNote={Robotic devices and functional electrical stimulation (FES) are utilized to provide rehabilitation therapy to persons with incomplete spinal cord injury. The goal of the therapy is to improve their weakened voluntary muscle strength. A variety of control strategies used in these therapies need a measure of participant's volitional strength. This informs the robotic or an FES device to modulate assistance proportional to the user's weakness. In this paper we propose an observer design to estimate ankle kinematics that are elicited volitionally. The observer uses a nonlinear continuous-time neuromuscular system, which has multi-rate sampled output measurements with non-uniform and unknown delays from various sensing modalities including electromyography, ultrasound imaging, and an inertial measurement unit. We assume an allowable maximum value of unsynchronized sampling intervals and nonuniform delays. By constructing a Lyapunov-Krasovskii function, sufficient conditions are derived to prove the exponential stability of the estimation error. Numerical simulations are provided to verify the effectiveness of the designed observer.}, booktitle={2019 American Control Conference (ACC)}, publisher={IEEE}, author={Zhang, Qiang and Sheng, Zhiyu and Kim, Kang and Sharma, Nitin}, year={2019}, month={Jul}, pages={872–877} }
 @book{zhang_xu_guo_xiao_2017, title={Design and modeling of a compact rotary series elastic actuator for an elbow rehabilitation robot}, volume={10464 LNAI}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85028355256&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-65298-6_5}, abstractNote={Rehabilitation robot has direct physical interaction with human body, in which the adaptability to interaction, safety and robustness is of great significance. In this paper, a compact rotary series elastic actuator (SEA) is proposed to develop an elbow rehabilitation robot for assisting stroke victims with upper limb impairments perform activities of daily living (ADLs). The compliant SEA ensures inherent safety and improves torque control at the elbow joint of this rehabilitation robot. After modeling of the rotary stiffness and dynamics of the SEA, a PD feedback plus feedforward control architecture is introduced. A test bench has been designed to experimentally characterize the performance of the proposed compliant actuator with controller. It shows an excellent torque tracking performance at low motion frequency, which can satisfy the elbow rehabilitation training requirement. These preliminary results can be readily extended to a full upper limb exoskeleton-type rehabilitation robot actuated by SEA without much difficulty.}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={Zhang, Q. and Xu, B. and Guo, Z. and Xiao, X.}, year={2017}, pages={44–56} }
 @article{zhang_wang_xiao_2016, title={Effects of Ground Compliance on Bipedal Robot Walking Dynamic Property}, volume={37}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85025162616&partnerID=MN8TOARS}, number={4}, journal={Journal of the Chinese Society of Mechanical Engineers, Transactions of the Chinese Institute of Engineers, Series C/Chung-Kuo Chi Hsueh Kung Ch'eng Hsuebo Pao}, author={Zhang, Q. and Wang, Y. and Xiao, X.-H.}, year={2016}, pages={335–342} }
 @book{zhang_xiao_guo_2016, title={Power efficiency-based stiffness optimization of a compliant actuator for underactuated bipedal robot}, volume={9834 LNCS}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84981200970&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-43506-0_16}, abstractNote={Introducing compliant actuation to robotic joints can obtain better disturbance rejection performance and higher power efficiency than conventional stiff actuated systems. In this paper, inspired by human joints, a novel compliant actuator applied to underactuated bipedal robot is proposed. After modeling the stiffness of the compliant actuator, this paper gives the configuration of the bipedal robot actuated by compliant actuators. Compared with the elastic structure of MABEL, the compliant element of our robot is simplified. Based on the dynamics of the compliant actuator-driven bipedal robot, a feedback linearization controller is presented to implement position control of the compliant actuator for power efficiency analysis and stiffness optimization. Co-simulations of MATLAB and ADAMS are performed under the defined control trajectory by altering actuator stiffness. The simulation results indicate that, compared with the actuator maintaining very high stiffness like a rigid actuator, the power efficiency of the compliant actuator is improved, and the stiffness optimized to 375 N•m/rad can reach the highest power efficiency.}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={Zhang, Q. and Xiao, X. and Guo, Z.}, year={2016}, pages={186–197} }
 @article{wang_zhang_xiao_2016, title={Trajectory tracking control of the bionic joint actuated by pneumatic artificial muscle based on robust modeling}, volume={38}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84964403691&partnerID=MN8TOARS}, DOI={10.13973/j.cnki.robot.2016.0248}, number={2}, journal={Jiqiren/Robot}, author={Wang, Y. and Zhang, Q. and Xiao, X.}, year={2016}, pages={248–256} }
 @book{zhang_teng_wang_xie_xiao_2015, title={A study of flexible energy-saving joint for biped robots considering sagittal plane motion}, volume={9245}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84951739824&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-22876-1_29}, abstractNote={A flexible ankle joint for biped walking robots is proposed to investigate the influence of joint stiffness on motor’s peak torque and energy consumption of the sagittal plane motion during the single support phase. Firstly, an improved model of the inverted pendulum is established, which is the theoretical foundation of the flexible ankle joint. Then the analysis of the analytic method of flexible joint is presented based on the improved model of the inverted pendulum. Finally, dynamic simulations of the flexible joint are performed to examine the correctness of analytic method. The results show that the flexible joint can reduce the joint motor’s peak torque and energy consumption. Furthermore, there is an optimal joint stiffness of the flexible system, which can minimum peak torque with reduction of 45.99% and energy consumption with reduction of 51.65%.}, journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, author={Zhang, Q. and Teng, L. and Wang, Y. and Xie, T. and Xiao, X.}, year={2015}, pages={333–344} }
 @article{zhang_xiao_wang_you_xie_2015, title={Compliant joint for biped robot considering energy consumption optimization}, volume={46}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84952033334&partnerID=MN8TOARS}, DOI={10.11817/j.issn.1672-7207.2015.11.014}, number={11}, journal={Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology)}, author={Zhang, Q. and Xiao, X. and Wang, Y. and You, P. and Xie, T.}, year={2015}, pages={4070–4076} }