Research and Publications - Department of Mechanical Engineering

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    Ultrasound-Based Sensing and Control of Functional Electrical Stimulation for Ankle Joint Dorsiflexion: Preliminary Study
    (Springer International Publishing Switzerland, 2022) Zhang, Qiang; Iyer, Ashwin; Sharma, Nitin
    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.
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    Ultrasound Imaging and Surface Electromyography-based Voluntary Effort Prediction and Control Strategies for Ankle Assistance
    (North Carolina State University, 2021) Zhang, Qiang
    The ankle joint plays a significant role in standing balance, walking, running, ascending, or descending stairs. However, impairments due to neuromuscular disorders, e.g., spinal cord injury, stroke, and multiple sclerosis, limit voluntary ankle power and severely undermine activities of daily living. These individuals usually have leftover or residual voluntary movement in their ankle muscles, which may not be enough to produce a normal gait. In these cases, powered ankle exoskeleton and functional electrical stimulation (FES) are potential interventions to assist ankle movements externally. Typically, control of these assistive devices is open-loop or trigger-based and largely ignores the residual ability. The key to restoring the normal function in the impaired ankle is to cooperatively control the assistive intervention with the user’s residual ankle effort. Therefore, incorporating physiological measurements of users’ residual ability is essential for the control of assistive devices. Mechanical sensors and surface electromyography (sEMG) signals are currently used to measure a user’s ankle joint volitional effort or motion intent. However, these sensors may not measure the ankle effort accurately due to several reasons, including frame misalignment, susceptibility to signal interference from neighboring muscles, and the inability to measure muscle contractions at deeper levels. Accurate estimation of the residual effort is critical for stable and effective control of desired ankle assistance. Therefore, a new reliable biological interface is required to predict residual ability more accurately and enable safe and therapeutically effective control of assistive devices. Recently, ultrasound (US) imaging has been proposed as a new sensing modality to visualize muscle contractions during voluntary limb movements directly. US imaging can potentially provide supplementary architectural-type information while sEMG signals provide electrical information for the same muscle contraction activity and thus to be used to predict the residual effort with much higher accuracy. However, US imaging features that are suitable in the prediction model are currently not fully developed, and closed-loop design to accommodate US imaging-derived measurements is non-existent. Furthermore, due to the high computational cost of analyzing US images, US-derived signals are unavailable in real-time or, at the most, are available at low sampling rates. Thus, its integration in real-time closed-loop control remains challenging. The first goal of this dissertation is to develop voluntary ankle effort prediction models that incorporate US imaging-derived signals and sEMG signals. The second goal of this dissertation is to design a new class of closed-loop controllers that incorporate low sampled US imaging-derived measurements signals of voluntary or FES-evoked muscle activity. For the first goal, I extracted multiple architectural and functional features from a high-dimensional US image to fuse with a low-dimensional sEMG signal. Then, I developed a modified Hill-type neuromuscular model and machine learning approaches to map neuromuscular signals (fused sEMG and US imaging features) to ankle joint kinetics and kinematics. For the second goal, I first derived an extended Kalman filter-type multi-rate observer to fuse high-sampled sEMG signal and low-sampled US imaging signal for the real-time closed-loop control of a cable-driven ankle exoskeleton. The dissertation also describes the design of a new bidirectional cable-driven ankle exoskeleton. The ankle exoskeleton assistance framework merges the multi-rate observer of sEMG and US imaging measurements with a newly derived adaptive impedance control that is based on the assist as-needed control principle. The controller is derived using a Lyapunov-based stability approach, which also guarantees its stability. Lastly, in the second goal, a sampled-data observer (SDO) is designed to transform the US imaging-derived measures of FES-evoked muscle activation, sampled at a low rate, to control FES of ankle dorsiflexion continuously. A dynamic surface control technique is derived to use the SDO transformed continuous signals and achieve the ankle joint trajectory tracking task. The combined controller and observer framework uses a Lyapunov-Krasovskii-based stability approach for its design and to guarantee the framework’s stability. The first outcome of this dissertation is the development of voluntary ankle effort prediction approaches that used fused features from sEMG and US imaging. The second outcome of this dissertation is the derivation of the US imaging-based closed-loop control frameworks for assisting the ankle joint with either an ankle exoskeleton or FES during walking. The control framework incorporates the voluntary or FES-evoked ankle torque in the control of powered ankle exoskeleton or FES. Multiple seated and treadmill walking experiments conducted on non-disabled participants validate the volitional effort prediction approaches and proposed control frameworks. These outcomes show a promising use of US imaging to predict residual ability when fused with existing sensing modalities such as sEMG and its potential clinical translation in the control of assistive devices such as FES and powered exoskeletons.
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    Shared Control of a Powered Exoskeleton and Functional Electrical Stimulation Using Iterative Learning
    (Frontiers, 2021) Molazadeh, Vahidreza; Zhang, Qiang; Bao, Xuefeng; Sharma, Nitin
    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.
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    Personalized Fusion of Ultrasound and Electromyography-derived Neuromuscular Features Increases Prediction Accuracy of Ankle Moment during Plantarflexion
    (Elsevier, 2022) Zhang, Qiang; Clark, William H.; Franz, Jason R.; Sharma, Nitin
    Objective: 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. Methods: 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. Results: 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. Conclusion: 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.
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    Ultrasound Echogenicity-based Assessment of Muscle Fatigue During Functional Electrical Stimulation
    (IEEE, 2021) Zhang, Qiang; Iyer, Ashwin; Lambeth, Krysten; Kim, Kang; Sharma, Nitin
    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 (R 2 =0.870±0.026) and dynamic (R 2 =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.
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    Imposing Healthy Hip Motion Pattern and Range by Exoskeleton Control for Individualized Assistance
    (IEEE, 2022) Zhang, Qiang; Nalam, Varun; Tu, Xikai; Li, Minhan; Si, Jennie; Lewek, Michael D.; Huang, Helen He
    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.
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    Ultrasound Echogenicity as an Indicator of Muscle Fatigue during Functional Electrical Stimulation
    (MDPI, 2022) Zhang, Qiang; Iyer, Ashwin; Lambeth, Krysten; Kim, Kang; Sharma, Nitin
    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.
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    An Iterative Learning Controller for a Switched Cooperative Allocation Strategy During Sit-to-Stand Tasks with a Hybrid Exoskeleton
    (IEEE, 2022) Molazadeh, Vahidreza; Zhang, Qiang; Bao, Xuefeng; Sharma, Nitin
    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.
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    Plantarflexion Moment Prediction during the Walking Stance Phase with an sEMG-Ultrasound Imaging-Driven Model
    (IEEE, 2021) Fragnito, Natalie; Zhang, Qiang; Myers, Alison; Sharma, Nitin
    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.
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    Fused Ultrasound and Electromyography‑Driven Neuromuscular Model to Improve Plantarflexion Moment Prediction Across Walking Speeds
    (Springer Nature, 2022) Zhang, Qiang; Fragnito, Natalie; Franz, Jason R.; Sharma, Nitin
    Background: Improving the prediction ability of a human-machine interface (HMI) is critical to accomplish a bio-inspired or model-based control strategy for rehabilitation interventions, which are of increased interest to assist limb function post neurological injuries. A fundamental role of the HMI is to accurately predict human intent by mapping signals from a mechanical sensor or surface electromyography (sEMG) sensor. These sensors are limited to measuring the resulting limb force or movement or the neural signal evoking the force. As the intermediate mapping in the HMI also depends on muscle contractility, a motivation exists to include architectural features of the muscle as surrogates of dynamic muscle movement, thus further improving the HMI's prediction accuracy. Objective: The purpose of this study is to investigate a non-invasive sEMG and ultrasound (US) imaging-driven Hill-type neuromuscular model (HNM) for net ankle joint plantar exion moment prediction. We hypothesize that the fusion of signals from sEMG and US imaging results in a more accurate net plantar exion moment prediction than sole sEMG or US imaging. Methods: Ten young non-disabled participants walked on a treadmill at speeds of 0.50, 0.75, 1.00, 1.25, and 1.50 m/s. The proposed HNM consists of two muscle-tendon units. The muscle activation for each unit was calculated as a weighted summation of the normalized sEMG signal and normalized muscle thickness signal from US imaging. The HNM calibration was performed under both single-speed mode and inter-speed mode, and then the calibrated HNM was validated across all walking speeds. Results: On average, the normalized moment prediction root mean square error was reduced by 14.58 % (p = 0:012) and 36.79 % (p < 0:001) with the proposed HNM when compared to sEMG-driven and US imaging-driven HNMs, respectively. Also, the calibrated models with data from the inter-speed mode were more robust than those from single-speed modes for the moment prediction. Conclusions: The proposed sEMG-US imaging-driven HNM can significantly improve the net plantar exion moment prediction accuracy across multiple walking speeds. The findings imply that the proposed HNM can be potentially used in bio-inspired control strategies for rehabilitative devices due to its superior prediction.
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    An Online Actor-Critic Identifier with Sampled Fatigue Measurements for Optimal Adaptive Control of FES and an Electric motor
    (IEEE, 2022) Iyer, Ashwin; Singh, Mayank; Zhang, Qiang; Sun, Ziyue; Sharma, Nitin
    Cooperative control of functional electrical stimulation (FES) and electric motors in a hybrid exoskeleton may benefit from fatigue measurements and online model learning. Recent model-based cooperative control approaches rely on time-consuming offline system identification of a complex musculoskeletal system. Further, they may lack the ability to include measurements from muscle sensors that monitor the FESinduced muscle fatigue, which may hinder maintaining desired muscle fatigue levels. This paper develops an online adaptive reinforcement learning approach to control knee extension via an electric motor and FES. An optimal tracking control problem that uses an actor-critic identifier structure is formulated to approximate an optimal solution to the Hamiltonian-Jacobi- Bellman equation. The continuous controller provides asymmetrically saturated optimal control inputs of FES and the electric motor. Critic and identifier neural networks are designed to simultaneously estimate the reward function and the system dynamics based on sampled fatigue measurements and compute control actions. Importantly, simulation results show that a satisfactory joint angle tracking and actuator allocation can be obtained at multiple on-demand desired muscle fatigue levels and prolong FES utilization.
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    Modeling and Control of a Cable-Driven Rotary Series Elastic Actuator for an Upper Limb Rehabilitation Robot
    (Frontier, 2020-02-24) Zhang, Qiang; Sun, Dingyang; Qian, Wei; Xiao, Xioahui; Guo, Zhao
    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.
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    Sampled-Data Observer Based Dynamic Surface Control of Delayed Neuromuscular Functional Electrical Stimulation
    (ASME, 2020-10) Zhang, Qiang; Iyer, Ashwin; Sun, Ziyue; Dodson, Albert; Sharma, Nitin
    Functional electrical stimulation (FES) is a potential technique for reanimating paralyzed muscles post neurological injury/ disease. Several technical challenges including difficulty in measuring and compensating for delayed muscle activation levels inhibit its satisfactory control performance. In this paper, an ultrasound (US) imaging approach is proposed to measure delayed muscle activation levels under the implementation of FES. Due to low sampling rates of US imaging, a sampled data observer (SDO) is designed to estimate the muscle activation in a continuous manner. The SDO is combined with continuous-time dynamic surface control (DSC) approach that compensates for the electromechanical delay (EMD) in the tibialis anterior (TA) activation dynamics. The stability analysis based on the Lyapunov-Krasovskii function proves that the SDObased DSC plus delay compensation (SDO-DSC-DC) approach achieves semi-global uniformly ultimately bounded (SGUUB) tracking performance. Simulation results on an ankle dorsiflexion neuromusculoskeletal system show the root mean square error (RMSE) of desired trajectory tracking is reduced by 19.77 % by using the proposed SDO-DSC-DC compared to the DSC-DC without the SDO. The findings provide potentials for rehabilitative devices, like powered exoskeleton and FES, to assist or enhance human limb movement based on the corresponding muscle activities in real-time.
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    Volitional Contractility Assessment of Plantar Flexors by Using Non-invasive Neuromuscular Measurements
    (IEEE, 2020-08-15) Zhang, Qiang; Iyer, Ashwin; Kim, Kang; Sharma, Nitin
    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 imaging derived 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.
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    A Dual-Modal Approach Using Electromyography and Sonomyography Improves Prediction of Dynamic Ankle Movement: A Case Study
    (IEEE, 2021) Zhang, Qiang; Iyer, Ashwin; Sun, Ziyue; Kim, Kang; Sharma, Nitin
    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 dorsiflexionmovement. 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.
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    Analysis of Tremor During Grasp Using Ultrasound Imaging: Preliminary Study
    (IEEE, 2020) Iyer, Ashwin; Sun, Zhiyu; Zhang, Qiang; Kim, Kang; Sharma, Nitin
    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.
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    A Tube-based Model Predictive Control Method for Joint Angle Tracking with Functional Electrical Stimulation and An Electric Motor Assist
    (IEEE, 2021-05) Sun, Ziyue; Bao, Xuefeng; Zhang, Qiang; Lambeth, Krysten; Sharma, Nitin
    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.
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    Energy Microfiche Collection
    (1984-12-10) Sandy, John H.
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    Design and Modeling of a Compact Rotary Series Elastic Actuator for an Elbow Rehabilitation Robot
    (Springer, 2017-08-06) Zhang, Qiang; Xu, Benyan; Guo, Zhao; Xiao, Xiachui
    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.
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    Observer Design for a Nonlinear Neuromuscular System with Multi-rate Sampled and Delayed Output Measurements
    (IEEE, 2019-07) Zhang, Qiang; Sheng, Zhiyu; Kim, Kang; Sharma, Nitin
    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.