Research and Publications - Department of Mechanical Engineering

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Prediction of Residual Stress and Part Distortion in Selective Laser Melting
(Elsevier, 2016-06-05) Li, C.; Liu, J.F.; Guo, Y.B.
Selective laser melting (SLM) is widely used to make functional metal parts. The high-temperature process will produce large tensile residual stress (RS) which leads to part distortion and poor product performance. Traditional modeling approaches are not practical to predict residual stress and part distortion due to the exceedingly high computational cost. In this study, two efficient multiscale modeling methods have been developed to across microscale laser scan, mesoscale layer hatch, and macroscale part buildup for fast prediction of residual stress and part distortion. A concept of equivalent heat source has been developed from the microscale laser scan model. In the “stress-thread” method, the local residual stress field was predicted by the mesoscale layer hatch model using the equivalent heat source, then the residual stress field is imported, i.e., “stress-thread”, to the macroscale part buildup model to predict residual stress and part distortion. In the temperature-thread method, the powder–liquid–solid material transition has been incorporated. A body heat flux obtained from the microscale laser scan model is applied, i.e., “temperature-thread”, to the hatch layer. Then multiple hatches are sequentially “deposited” in the macroscale part buildup model with different scanning strategies. The predicted part distortions by both methods were compared and compared with the experimental data.
Energy Consumption in Additive Manufacturing of Metal Parts
(Elsevier, 2018-08-03) Liu, Z.Y.; Li, C.; Fang, X.Y.; Guo, Y.B.
Additive manufacturing (AM) has been widely used to fabricate metal part by using high energy beam to fully melt feed stock materials layer-upon-layer directly from a digital CAD model. Energy consumption in metal AM could not only affects the sustainability of the process itself but also influences the microstructure and mechanical properties of the fabricated components. This paper first summarizes the current research status of energy consumption on machine and process levels. On the machine level, energy consumption by subsystems of the AM machine tool such as high energy beam generator, control system, cooling system etc. and at different operation modes are discussed. On the process level, the energy flow distribution in typical AM processes is first analyzed, then research efforts to quantify the energy flow in AM processes are highlighted. The life cycle assessment of energy consumption of AM metal parts along with energy consumption reduction strategies is also thoroughly discussed.
Surface Integrity Characteristics in Wire-EDM of Inconel 718 at Different Discharge Energy
(Elsevier, 2013-03-26) Li, L.; Guo, Y.B.; Wei, X.T.; Li, W.
Superalloys such as Inconel 718 are widely used in turbomachinery industry due to their outstanding mechanical properties. Inconel alloys are very difficult to machine using conventional mechanical processes like broaching, milling or grinding. Wire electrical discharge machining (W-EDM) is an alternative competitive process to manufacture complex Inconel part geometries. However, surface integrity of W-EDMed Inconel components is poorly understood. This study presents the characteristics of surface integrity vs. discharge energy in W-EDM of Inconel 718. The results show that the EDMed surface topography shows dominant coral reef microstructures at high discharge energy, while random micro voids are dominant at low discharge energy. Surface roughness is equivalent for parallel and perpendicular wire directions, and average roughness can be significantly reduced for low discharge energy. The thick white layers are predominantly discontinuous and non-uniform at relative high discharge energy. Micro voids are confined within the thick white layers and no micro cracks were found in the subsurface. The thin white layers by trim cut at low discharge energy become more continuous, uniform, and are free of micro voids. Compared to the bulk material, white layers have dramatic reduction in microhardness due to significant thermal degradation. In addition, surface alloying from wire electrode and water dielectric are obvious in main cut at high discharge energy, but it can be minimized in trim cuts at very low discharge energy.
Residual Stress in Metal Additive Manufacturing
(Elsevier, 2018-06-06) Li, C.; Liu, Z.Y.; Fang, X.Y.; Guo, Y.B.
Additive manufacturing (AM) has been widely used to fabricate functional metal parts in automobile, aerospace, energy, and medical device industries due to its flexible process capacity including complex geometry, functional graded materials, and free usage of tool. For the two major categories of metal additive manufacturing processes include powder bed fusion (PBF) and directed energy deposition (DED), parts are fabricated through melting of feed stock materials in the form of either powders or wires directly from a CAD model. The unique thermal cycle of metal additive manufacturing is characterized by: (1) rapid heating rate due to high energy intensity with steep temperature gradients; (2) rapid solidification with high cooling rates due to the small volume of melt pool; and (3) melt-back involving simultaneous melting of the top powder layer and re-melting of underlying previously solidified layers. Residual stress caused by the unique thermal cycle in AM is the critical issue for the fabricated metal parts since the steep residual stress gradients generate part distortion which dramatically deteriorate functionality of the end-use parts. This paper comprehensively assessed the current research status on residual stress sources, characteristics, and mitigation. First, the relationship between residual stress and microstructure is highlighted in AM metal parts. Then, the measurement methods and characteristics of residual stress in both as-build metal parts and post-processed ones were summarized. Third, residual stress mitigation and control methods including in-situ and post-process control methods were thoroughly discussed. Furthermore, future work directions are provided in this work.
Direct recycling of machine chips through a novel solid-state additive manufacturing process
(Elsevier, 2020) Jordon, J.B.; Allison, P.G.; Phillips, B.J.; Avery, D.Z.; Kinser, R.P.; Brewer, L.N.; Cox, Chase; Doherty, K.
Recycling of metal waste for feedstock material in additive manufacturing (AM) is typically carried out through energy extensive melting and solidification processing techniques. However, in austere environments, energy production can be limited and thus melting down scrap into powder through an atomization process is not a viable approach to producing feedstock from reclaimed waste. However, advancements in solid-state material processing has led to a novel additive manufacturing process that uses solid, macroscale structural feed-rods that allow for high deposition rates with wrought-like mechanical properties. This process, additive friction stir deposition (AFS-D), leverages friction between the rotating feed-rod and the substrate (or previous deposition layer) to soften and promote material flow that creates a plasticized and metallurgically bonded material layer. As such, the AFS-D approach is ideally suited to directly process metal waste with minimal material preparation. To illustrate this concept, we demonstrate AM builds with refined microstructure and wrought-like properties made using loose machines chips. The results of this study demonstrate the potential to recycle metal machine chips by feeding them directly into the AFS-D process and produce structurally sound depositions that can be used for Point-of-Need manufacturing within austere environments.
Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation
(IEEE, 2020) Khodaei, Mohammad Javad; Candelino, Nicholas; Mehrvarz, Amin; Jalili, Nader
Over the past decade, there has been an unprecedented international focus on improved quality and availability of medical care, which has reignited interest in clinical automation and drawn researchers toward novel solutions in the field of physiological closed-loop control systems (PCLCs). Today, multidisciplinary groups of expert scientists, engineers, clinicians, mathematicians, and policy-makers are combining their knowledge and experience to develop both the next generation of PCLC-based medical equipment and a collaborative commercial/academic infrastructure to support this rapidly expanding frontier. In the following article, we provide a robust introduction to the various aspects of this growing field motivated by the recent and ongoing work supporting two leading technologies: the artificial pancreas (AP) and automated anesthesia. Following a brief high-level overview of the main concepts in automated therapy and some relevant tools from systems and control theory, we explore – separately – the developments, challenges, state-of-the-art, and probable directions for AP and automated anesthesia systems. We then close the review with a consideration of the common lessons gleaned from these ventures and the implications they present for future investigations and adjacent research.
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.

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