Research and Publications - Department of Mechanical Engineering

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Biomechanics-Informed Mechatronics Design of Comfort-Centered Portable Hip Exoskeleton: Actuator, Wearable Interface, Controller
(IEEE, 2025) Rodríguez-Jorge, Daniel; Zhang, Sainan; Huang, Jin Sen; Lopez-Sanchez, Ivan; Srinivasan, Nitin; Zhang, Qiang; Zhou, Xianlian; Su, Hao
Exoskeletons can improve human mobility, but discomfort remains a significant barrier to their widespread adoption. This paper presents a comfort-centered mechatronics design of portable hip exoskeletons, comprising of three factors: (i) actuation, (ii) wearable interface, (iii) and assistive controller. We introduced an analytical multibody model to predict the human-exoskeleton contact forces during gait. Informed by this model, we designed a wearable interface that significantly improved the three considered objective metrics: (i) undesired contact forces at the wearable interface, (ii) wobbling, and (iii) metabolic reduction, and also the post-test evaluation via a System Usability Scale questionnaire as a subjective metric. Our experiments with two exoskeleton controllers (gait-based and reinforcement learning-based) demonstrated that the design of the wearable physical interface has a greater impact on reducing metabolic rate and minimizing wobbling than the choice of controller. Our actuation design method leads to highly backdrivable, lightweight quasi-direct drive actuators with high torque tracking performance. By leveraging this wearable design, we achieved up to 60% reduction in undesired contact forces, and a 74% reduction in exoskeleton wobbling in the frontal axis compared to a traditional configuration. Additionally, the net metabolic cost reduction was 18% compared to the no exoskeleton condition.
A Modular Cable-Driven Pelvic Robot for Rehabilitation Training: Design, Adaptive Control, and Characterization
(IEEE, 2025) Akinniyi, Oluwasegun; Zhao, Pan; Martelli, Dario; Zhang, Qiang
Accurate monitoring and control of applied forces on human subjects are essential for safe and effective cable-driven robot-assisted rehabilitation. Managing external disturbances in these interventions, especially with human subjects as end-effectors, is challenging. This study presents a novel L1 adaptive controller (L1AC) for a cable-driven pelvic robot (CPR) capable of applying waist-pull perturbations up to 120 N. The modular design allows for easy setup in both anterior-posterior and medial-lateral directions. The L1AC , designed to minimize external disturbances, was rigorously tested in simulations and experiments. Results showed improved force tracking for a 60 N, 0.5 Hz sinusoidal force profile. In a fixed rigid object scenario, the controller achieved an RMSE of 6.20 ± 0.26 N, compared to 7.55 ± 0.33 N with the full-state feedback reference input (FSFRI) controller, and 6.83 ± 0.11 N with the H-infinity (H∞) controller. For the fixed semi-rigid object scenario, the RMSE was 7.58 ± 0.07 N, compared to 9.80 ± 0.10 N with FSFRI controller, and 8.38 ± 0.16 N with H∞ controller. In human-standing scenarios, it achieved an RMSE of 5.84 ± 0.34 N, compared to 6.20 ± 0.44 N with the FSFRI controller, and 7.42 ± 0.12 N with H∞ controller. The proposed controller was further tested during gait-synchronized waist-pull perturbation walking experiments under five conditions. Electromyographic signals from the calf muscles and lower limb motion capture data from all walking conditions revealed reduced muscle activation and joint motion as perturbation forces increased, demonstrating subjects’ adaptive responses. These findings emphasize the controller’s robustness in force trajectory tracking and its potential to facilitate human motor adaptation, offering significant promise for enhancing rehabilitation strategies using cable-driven robotic systems
Toward Sensor Fusion Neuromuscular Interface for Continuous Finger Joint Angle Estimation via Deep Transfer Learning
(IEEE, 2025) Chen, Yun; Zhang, Xinyu; He, Hongsheng; Pu, Lina; Shou, Wan; Zhang, Qiang
Accurate decoding of motor intent from biosignals is essential for controlling upper-limb prostheses. We proposed a novel high-dimensional multimodal deep learning framework that fuses surface electromyography (sEMG) and B-mode ultrasound (US) images to estimate metacarpophalangeal and proximal interphalangeal joint angles continuously. The framework employs a shared Encoder– Decoder–Regression architecture integrating transposed convolutions, multi-head cross-attention, and long short-term memory layers to jointly capture spatiotemporal features from both modalities. In this model, each modality is processed by its own encoder and decoder, and the resulting feature maps are fused before being passed to the regression head. To improve cross-subject generalization and reduce data requirements for new users, we introduce a transfer learning strategy with parameter freezing. Experiments on data from seven subjects show that, compared with sEMG-only and US-only baselines, the fusion model reduces test RMSE by 1.873◦ (21.02%) and 0.794◦ (10.15%), and increases test local correlation by 0.069 (10.02%) and 0.039 (5.48%) (p < 0.05), demonstrating the potential of multimodal fusion for amputee rehabilitation. Ablation studies further confirm that the full CNN+LSTM+Attention model achieves the best performance, reducing test RMSE by 2.022◦ (22.32%) and increasing test local correlation by 0.053 (7.52%) (p < 0.05). Furthermore, fine-tuning the pretrained model with only 25% of a new subject’s data yields performance comparable to full retraining, highlighting the framework’s data efficiency.
Controlled Robot Language with Frame Semantics (FrameCRL) for Autonomous Context-Aware High-Level Planning
(IEEE, 2025) Tran, Dang; Yan, Fujian; Zhang, Qiang; Zhang, Yinlong; He, Hongsheng
This paper proposes a con gurable and scalable framework based on Controlled Robot Language with Frame Semantics (FrameCRL) for plan generation. Given natural language instructions, FrameCRL constructs an equivalent formal semantic formulation in the form of discourse representation structures (DRS). Imperative verbs are extracted from the semantic structures as keys to anchor relevant semantic frames from FrameNet, and the selected semantic frames are used to construct goal statements in planning language. Nonimperative statements are further analyzed to generate object speci cations and the initial state of the planning problem. These generated statements are then merged into a single planning script, which can be solved directly by the integrated planner. The performance of FrameCRL was evaluated on various natural language corpora and compared with large language models (LLM) based methods in plan generation. The results demonstrated the outperformance of FrameCRL in generating high-quality plans and its capability to handle large context scenarios. The FrameCRL was also tested on pick-andplace tasks using a dual-arm robot and it showcased a robust performance in linguistic understanding.
Towards Neurorobotic Interface for Finger Joint Angle Estimation: A Multi-Stage CNN-LSTM Network with Transfer Learning
(2025)
To maximize the autonomy of individuals with upper limb amputations in daily activities, leveraging forearm muscle information to infer movement intent is a promising research direction. While current prosthetic hand technologies can utilize forearm muscle data to achieve basic movements such as grasping, accurately estimating finger joint angles remains a significant challenge. Therefore, we propose a Multi-Stage Cascade Convolutional Neural Network with Long Short-Term Memory Network, where an upsampling module is introduced before the downsampling module to enhance model generalization. Additionally, we designed a transfer learning (TL) framework based on parameter freezing, where the pre-trained downsampling module is fixed, and only the upsampling module is updated with a small amount of out-ofdistribution data to achieve TL. Furthermore, we compared the performance of unimodal and multimodal models, collecting surface electromyography (sEMG) signals, brightness mode ultrasound images (B-mode US images), and motion capture data simultaneously. The results show that on the validation set, the US image had the lowest error, while on the prediction set, the four-channel sEMG achieved the lowest error. The performance of the multimodal model in both datasets was intermediate between the unimodal models. On the prediction set, the average normalized root mean square error values for the four-channel sEMG, US images, and sensor fusion models across three subjects were 0.170, 0.203, and 0.186, respectively. By utilizing advanced sensor fusion techniques and TL, our approach can reduce the need for extensive data collection and training for new users, making prosthetic control more accessible and adaptable to individual needs.
Enhancing Prosthetic Control with Ultrasound Images: A Convolutional Neural Network Approach for Hand Gesture Recognition
(Elsevier, 2024) Chen, Yun; Bao, Xuefeng; He, Hongsheng; Zhang, Qiang
Human hand gesture recognition using biological signals from the forearm is an increasingly significant area of research, with implications across various fields such as prosthetic development, rehabilitation, and human-machine interaction. However, traditional hand gesture recognition with surface electromyography (sEMG) technique has some challenges, including cross-talk from neighboring muscles, low signal-to-noise ratio, and inability to measure deep muscles. In the current study, we proposed to use brightness mode (B-mode) ultrasound images from the muscles of the forearm anterior side as an alternative neuromuscular interface to recognize hand gestures. We designed a convolutional neural network (CNN) classifier to build the personalized mapping from static ultrasound images to eight different hand gestures. To evaluate the performance of the proposed CNN classifier, an ultrasound images dataset and labeled gestures from four young healthy participants were collected and analyzed. Results from offline intra-subject (personalization) validation, quasi-real-time validation, and real-time validation showed high classification accuracy of 99.65%, 97.47%, and 90.83%, respectively. In addition, real-time hand gesture recognition could be executed within 50 ms per image frame by using the proposed CNN classifier. Our findings demonstrated promising real-time hand gesture recognition with high accuracy by using B-mode ultrasound images and the proposed CNN classifier for prosthetic hand control.
Lower Limb Muscle Activities and Contractility during Stepping Response to Unexpected Waist-Pull Perturbations
(IEEE, 2025-05) Akinniyi, Oluwasegun; Gainey, Maxwell; Martelli, Dario; Zhang, Qiang
Reactive balance responses, which involve corrective and protective strategies, are highly dependent on rapid muscle activation to restore postural stability. Although electromyography (EMG) is commonly used to measure muscle activity, it has limitations such as signal interference, particularly during fast responses to external disturbances. Ultrasound imaging (US), in contrast, provides visualization of both superficial and deep muscles. Combining EMG and US imaging offers complementary insight into muscle behavior during reactive balance tasks. In this study, we investigated muscle activation and fascicle length changes in the medial gastrocnemius (MGS), lateral gastrocnemius (LGS), and soleus (SOL) muscles of the dominant (stepping) leg during stepping responses to unexpected low-amplitude (57.6 ± 5.8 N) and high-amplitude (123.4 ± 11.1 N) waist-pull perturbations in the anterior and posterior directions. Five young male adults (age: 25.2 ± 5.5 years) participated in the study. Results showed that perturbation amplitude significantly affected the EMG activation of both the MGS and SOL muscles in both directions, consistent with previous studies. Similarly, perturbation amplitude impacted fascicle length shortening in the LGS and SOL muscles. Significant differences in MGS and SOL activation were observed between high-amplitude and lowamplitude perturbations in both directions. Fascicle shortening in the LGS also differed significantly between perturbation amplitudes, whereas SOL fascicle shortening did not. By combining EMG and US imaging within the same participants, this study provides new insights into the neuromuscular mechanisms underlying balance control. These findings may inform the development of improved control strategies for neurorehabilitation devices and fall-prevention systems.
AnkleImage - An ultrafast ultrasound image dataset to understand the ankle joint muscle contractility
(Elsevier, 2024-12-27) Zhang, Qiang; Hakam, Noor; Akinniyi, Oluwasegum; Iyer, Ashwin; Bao, Xuefeng; Sharma, Nitin
The role of the human ankle joint in activities of daily living, including walking, maintaining balance, and participating in sports, is of paramount importance. Ankle joint dorsiflexion and plantarflexion functionalities mainly account for ground clearance and propulsion power generation during locomotion tasks, where those functionalities are driven by the contraction of ankle joint skeleton muscles. Studies of corresponding muscle contractility during ankle dynamic functions will facilitate us to better understand the joint torque/power generation mechanism, better diagnose potential muscular disorders on the ankle joint, or better develop wearable assistive/rehabilitative robotic devices that assist in community ambulation. This data descriptor reports a new dataset that includes the ankle joint kinematics/kinetics, associated muscle surface electromyography, and ultrafast ultrasound images with various annotations, such as pennation angle, fascicle length, tissue displacements, echogenicity, and muscle thickness, of ten healthy participants when performing volitional isometric, isokinetic, and dynamic ankle joint functions (walking at multiple treadmill speeds, including 0.50 m/s, 0.75 m/s, 1.00 m/s, 1.25 m/s, and 1.50 m/s). Data were recorded by a research-use ultrasound machine, a self-designed ankle testbed, an inertia measurement unit system, a Vicon motion capture system, a surface electromyography system, and an instrumented treadmill. The descriptor in this work presents the results of a data curation or collection exercise from previous works, rather than describing a novel primary/experimental data collection.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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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