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Browsing by Author "Zhang, Qiang"

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A Clustering-based Method for Estimating Pennation Angle from B-mode Ultrasound Images
(Cambridge University Press, 2023-03-01) Bao, Xuefeng; Zhang, Qiang; Fragnito, Natalie; Wang, Jian; Sharma, Nitin
B-mode ultrasound (US) is often used to non-invasively measure skeletal muscle architecture, which contains human intent information. Extracted features from B-mode images can help improve closed-loop human-robotic interaction control when using rehabilitation/assistive devices. The traditional manual approach to inferring the muscle structural features from US images is laborious, time-consuming, and subjective among different investigators. This paper proposes a clustering-based detection method that can mimic a well-trained human expert in identifying fascicle and aponeurosis and, therefore, compute the pennation angle (PA). The clustering-based architecture assumes muscle fibers have tubular characteristics. It is robust for low-frequency image streams. We compared the proposed algorithm to two mature benchmark techniques: UltraTrack and ImageJ. The performance of the proposed approach showed higher accuracy in our dataset (frame frequency is 20Hz), i.e., similar to the human expert. The proposed method shows promising potential in automatic muscle fascicle orientation detection to facilitate implementations in biomechanics modeling, rehabilitation robot control design, and neuromuscular disease diagnosis with low-frequency data stream.
A Deep Learning Method to Predict Ankle Joint Moment During Walking at Different Speeds with Ultrasound Imaging: A Framework for Assistive Devices Control
(Cambridge University Press, 2022) Zhang, Qiang; Fragnito, Natalie; Bao, Xuefeng; Sharma, Nitin
Robotic assistive or rehabilitative devices are promising aids for people with neurological disorders as they help regain normative functions for both upper and lower limbs. However, it remains challenging to accurately estimate human intent or residual efforts non-invasively when using these robotic devices. In this article, we propose a deep learning approach that uses a brightness mode, that is, B-mode, of ultrasound (US) imaging from skeletal muscles to predict the ankle joint net plantarflexion moment while walking. The designed structure of customized deep convolutional neural networks (CNNs) guarantees the convergence and robustness of the deep learning approach. We investigated the influence of the US imaging’s region of interest (ROI) on the net plantarflexion moment prediction performance.We also compared the CNN-based moment prediction performance utilizing B-mode US and sEMG spectrum imaging with the same ROI size. Experimental results from eight young participants walking on a treadmill at multiple speeds verified an improved accuracy by using the proposed US imaging þ deep learning approach for net joint moment prediction.With the same CNN structure, compared to the prediction performance by using sEMG spectrum imaging, US imaging significantly reduced the normalized prediction root mean square error by 37.55% (p < .001) and increased the prediction coefficient of determination by 20.13% (p < .001). The findings show that the US imaging þ deep learning approach personalizes the assessment of human joint voluntary effort, which can be incorporated with assistive or rehabilitative devices to improve clinical performance based on the assistas- needed control strategy.
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.
A Robotic Assistance Personalization Control Approach of Hip Exoskeletons for Gait Symmetry Improvement
(IEEE, 2023) Zhang, Qiang; Tu, Xikai; Si, Jennie; Lewek, Michael D.; Huang, He
Healthy human locomotion functions with good gait symmetry depend on rhythmic coordination of the left and right legs, which can be deteriorated by neurological disorders like stroke and spinal cord injury. Powered exoskeletons are promising devices to improve impaired people's locomotion functions, like gait symmetry. However, given higher uncertainties and the time-varying nature of human-robot interaction, providing personalized robotic assistance from exoskeletons to achieve the best gait symmetry is challenging, especially for people with neurological disorders. In this paper, we propose a hierarchical control framework for a bilateral hip exoskeleton to provide the adaptive optimal hip joint assistance with a control objective of imposing the desired gait symmetry during walking. Three control levels are included in the hierarchical framework, including the high-level control to tune three control parameters based on a policy iteration reinforcement learning approach, the middle-level control to define the desired assistive torque profile based on a delayed output feedback control method, and the low-level control to achieve a good torque trajectory tracking performance. To evaluate the feasibility of the proposed control framework, five healthy young participants are recruited for treadmill walking experiments, where an artificial gait asymmetry is imitated as the hemiparesis post-stroke, and only the ‘paretic’ hip joint is controlled with the proposed framework. The pilot experimental studies demonstrate that the hierarchical control framework for the hip exoskeleton successfully (asymmetry index from 8.8% to − 0.5%) and efficiently (less than 4 minutes) achieved the desired gait symmetry by providing adaptive optimal assistance on the ‘paretic’ hip joint.
A Study of Flexible Energy-Saving Joint for Biped Robots Considering Sagittal Plane Motion
(Springer International Publishing Switzerland, 2015-08) Zhang, Qiang; Teng, Lin; Wang, Yang; Xie, Tao; Xiao, Xiaohui
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%.
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.
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.
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.
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.
Ankle Dorsiflexion Strength Monitoring by Combining Sonomyography and Electromyography
(IEEE Explore, 2019) Zhang, Qiang; Sheng, Zhiyu; Moore-Clingenpeel, Frank; Kim, Kang; Sharma, Nitin
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.
AnkleImage - An ultrafast ultrasound image dataset to understand the ankle joint muscle contractility (See Full Item Page for link to data)
(2024) Zhang, Qiang; Akinniyi, Oluwasegun
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.
Closing the Loop Between Wearable Robots and Machine Learning: A New Paradigm for Steering Assistance Personalization Control
(Springer Nature, 2024-07-24) Zhang, Qiang; Zanotte, Damiano; Sharifi, Motjaba; Kim, Myunghee; Li, Zhijun
Lower-extremity wearable robotic devices, first introduced in the early 2000s, have been developed to enhance human mobility and support therapeutic training for patients. Recent advancements in human-in-the-loop (HIL) optimization have significantly improved the control of these devices, fine-tuning the interaction between humans and robots. This has led to more personalized assistance for daily living activities and rehabilitation training. Our comprehensive and extensive literature review, spanning from January 2017 to December 2023, highlights 34 noteworthy studies that have demonstrated enhanced human locomotion performance through HIL-optimized and personalized assistance. This review explores pivotal innovations and methodologies for controlling lower-extremity robotic exoskeletons, exosuits, and prostheses. It covers the establishment of control objectives, the application of various optimization methods, and the assessment of outcomes. Additionally, we provide a comparative analysis of the HIL optimization method against alternative control strategies, such as those based on reinforcement learning. Looking forward, we discuss expected trends that aim to enhance the efficacy of wearable robotic devices. We also recognize the challenges that need to be addressed to fully realize the benefits of customized gait assistance for individuals with lower-extremity impairments or neurological conditions. This includes technological, regulatory, and user-centered issues that could impact the widespread adoption and effectiveness of these innovative systems.
Compliant Joint for Bipod Robot Considering Energy Consumption Optimization
(Springer, 2015-11) Zhang, Qiang; Xiao, Xiaohui; Wang, Yang; You, Penghui; Xie, Tao
Abstract: In order to optimize the energy consumption of biped robot while walking, the compliant joint for biped walking robots is proposed to investigate the influence of ankle joint and knee joint stiffness on motor torque and energy consumption of the sagittal plane motion during the single support phase. Firstly, an improved model of the five-link biped robot is established, which is the theoretical foundation of the compliant joint. Then, with the method of gait planning based on natural Zero Moment Point (ZMP) trajectory, the robot’s center of mass (COM) track is obtained by setting reference of ZMP trajectory and the gait on a rigid path is acquired by interpolation. Finally, both the Lagrange equations analytic method and dynamic simulations are performed to analyze the influences of compliant joint stiffness on motor torque and energy consumption based on the improved model of the five-link biped robot. The results show that the compliant joint can reduce the joint motor torque and energy consumption effectively. Furthermore, there is an optimal stiffness of the compliant ankle joint and knee joint respectively, which can minimum the motor energy consumption with reduction of 89.87% and 90.11% in analytic method, as well as 88.66% and 81.23% in dynamic simulations.
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.
Development and Control of a Cable-Driven Robotic Platform for Studying Human Balance and Gait
(IEEE, 2024) Akinniyi, Oluwasegun; Sharifi, Mojtaba; Martelli, Dario; Zhang, Qiang
Aging is one of the main causes of weakness in mobility and a high risk of falling due to the degradation of neuromuscular and skeletal systems. Tremendous cable-driven robotic assistive devices have been proposed in recent years with the goal of fall risk mitigation and rehabilitation interventions. However, most of them require sophisticated structure and mechatronics design, leading to a relatively bulky nature. In this study, we developed a cable-driven robotic platform for waist perturbation. A lightweight load cell is installed between the end of the cable and a wearable waist belt to measure the pulling force in real time. A closed-loop adaptive full-state feedback control with reference input is proposed to guarantee good torque trajectory tracking performance. Preliminary benchtop and human subject testing with the proposed controller demonstrated an improved force tracking performance of sinusoidal force profiles ranging from 20 N to 80 N, with Root Mean Square Error (RMSE) values of 2.6 N to 10.6 N during fixed-object perturbations and 3.4 N ± 0.2 N to 12.7 N ± 1.0 N during standing perturbations, respectively, as compared to a RMSE of 5.6 N to 21.4 N and 7.1 N ± 0.6 N to 33.7 N ± 2.9 N with the traditional proportional-integral-derivative controller using the same force profile and magnitudes, and under the same perturbation conditions. The hardware and control development of this robotic platform will be used for balance perturbation studies during static standing and human-in-the-loop optimization control studies during dynamic walking tasks.
Editorial: Assistance personalization/customization for human locomotion tasks by using wearable lower-limb robotic devices
(Frontiers, 2024) Zhang, Qiang; Bao, Xuefeng; Guo, Zhao; Lv, Ge; Kim, Myunghee
Evaluation of a Fused Sonomyography and Electromyography-Based Control on a Cable-Driven Ankle Exoskeleton
(IEEE, 2023) Zhang, Qiang; Lambeth, Krysten; Sun, Ziyue; Dodson, Albert; Bao, Xuefeng; Sharma, Nitin
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.
Evaluation of Non-Invasive Ankle Joint Effort Prediction Methods for Use in Neurorehabilitation Using Electromyography and Ultrasound Imaging
(IEEE, 2021) Zhang, Qiang; Iyer, Ashwin; Kim, Kang; Sharma, Nitin
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.
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.
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.

Browsing by Author "Zhang, Qiang"

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