Department of Mechanical Engineering

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3D Numerical Modelling of a Rarefied Gas Flow in the Nearby Atmosphere around a Rotating Cometary Nucleus
(2007) Volkov, Alexey N.; Lukyanov, German A.; University of Alabama Tuscaloosa
A combined 3D model of a nearby atmosphere around an arbitrary rotating spherical cometary nucleus is developed. The model includes a 3D unsteady model of solar radiation absorption and heating of the nucleus material (water ice), its evaporation and condensation and a 3D quasi-stationary kinetic model of flow inside the near nucleus coma. This model can be used to predict the coma flow in conditions typical for rendezvous projects such as ESA project Rossetta. Calculations are carried out with the help of this model to reveal the influence of nucleus rotation on its temperature field and the flow field in the nearby atmosphere. It was found that the nucleus rotation influences significantly the nucleus temperature field and the coma flow. Vapor flow around a rotating nucleus is essentially three dimensional and differs qualitatively from the coma flow around a non-rotating nucleus.
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.
Acoustic methods for regionalizing an impact force acting on a helmet structure
(University of Alabama Libraries, 2018) Davis, Jacob; Shepard, W. Steve; University of Alabama Tuscaloosa
It is often desired to know the location and magnitude of a force acting on a structure. Unfortunately, it is not always possible or desirable to install a sensor at the force location, such as when the force location is unknown or when the application of a force sensor would change the force transmission characteristics. A structure subjected to an impact has many different vibrational modes that are excited to different levels based on the excitation location. These vibrations decay with time depending on their different rates of modal damping and the associated acoustic radiation characteristics. This response of the structure can be measured and used to inversely reconstruct the input force. It is theoretically possible to use acoustic measurements for force reconstruction, but the method involved would be extremely difficult. In this study, approaches that are much simpler and easier to implement were considered. Acoustic signatures for several structure impact locations were measured, normalized relative to the force magnitude, and processed to examine the ability to correlate the acoustic signal to the force impact location. Various processing techniques, such as the Short Time Fourier Transform, were considered. A primary interest focused on the ability of using single-number metrics that describe features of the acoustic signature to aid in identifying the force location. For the experiments, a football helmet structure was used and multiple impact locations on the helmet were tested. The ability of these acoustic signatures, including those processed into single-number metrics, to aid in identifying the impact location was assessed.
Age-related differences in gait adaptations during overground walking with and without visual perturbations using a virtual reality headset
(Nature Portfolio, 2020) Osaba, Muyinat Y.; Martelli, Dario; Prado, Antonio; Agrawal, Sunil K.; Lalwani, Anil K.; Columbia University; University of Alabama Tuscaloosa; NewYork-Presbyterian Hospital
Older adults have difficulty adapting to new visual information, posing a challenge to maintain balance during walking. Virtual reality can be used to study gait adaptability in response to discordant sensorimotor stimulations. This study aimed to investigate age-related modifications and propensity for visuomotor adaptations due to continuous visual perturbations during overground walking in a virtual reality headset. Twenty old and twelve young subjects walked on an instrumented walkway in real and virtual environments while reacting to antero-posterior and medio-lateral oscillations of the visual field. Mean and variability of spatiotemporal gait parameters were calculated during the first and fifth minutes of walking. A 3-way mixed-design ANOVA was performed to determine the main and interaction effects of group, condition and time. Both groups modified gait similarly, but older adults walked with shorter and slower strides and did not reduce stride velocity or increase stride width variability during medio-lateral perturbations. This may be related to a more conservative and anticipatory strategy as well as a reduced perception of the optic flow. Over time, participants adapted similarly to the perturbations but only younger participants reduced their stride velocity variability. Results provide novel evidence of age- and context-dependent visuomotor adaptations in response to visual perturbations during overground walking and may help to establish new methods for early identification and remediation of gait deficits.
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 and Elicitation of Electroencephalogram Data Pertaining to High Alert and Stressful Situations: Source Localization Through the Inverse Problem
(University of Alabama Libraries, 2021) Heim, Isaac C; Fonseca, Daniel J.; University of Alabama Tuscaloosa
This dissertation work deals with the design and development of a fuzzy controller to analyze electroencephalogram (EEG) data. The fuzzy controller made use of the multiple functions associated with the different regions of the brain to correlate multiple Brodmann areas to multiple outputs. This controller was designed to adapt to any data imported into it. The current framework implemented supports a math study and a police officer study. The rules for the interactions of the Brodmann areas have been set up for these applications, detailing how well the police subjects’ brains exhibited behavior indicative to activation relating to vision, memory, shape/distance, hearing/sound, and theory of mind. The math subjects’ outputs were attuned to their related study which involved transcranial direct current stimulation (tDCS), which is a form of neurostimulation. Anode affinity, cathode affinity, calculation, memory, and decision making were the outputs focused on for the math study. This task is best suited to a fuzzy controller since interactions between Brodmann areas can be analyzed and the contributions of each area accounted for.The goal of the controller was to determine long-term behavior of the subjects with repeated sampling. With each addition of data, the controller was able to develop new bounds related to the current condition of the data in the study. Processing this data was accomplished by the creation of an automated filtering script for EEGLAB in MATLAB. The script was designed to rapidly load and filter the files associated with any given dataset. These files were also automatically prepared for analysis with a program called Low Resolution Brain Electromagnetic Tomography i.e. (LORETA). LORETA was used to solve the inverse problem, which involves identifying where the signals from the surface electrodes originated within the brain through a process called source localization. Once the sources of the EEG signals were located, they were associated with the Brodmann areas. The fuzzy controller then processed this information to automatically generate heat maps which displayed information such as normalized data, z-score, and rankings. Each set of scores displays how the subject's brain was acting, which lined up with the expected results.
Analysis of a natural gas combined cycle powerplant modeled for carbon capture with variance of oxy-combustion characteristics
(University of Alabama Libraries, 2011) Breshears, Matthew Joseph; Midkiff, K. Clark; University of Alabama Tuscaloosa
The world's ever growing demand for energy has resulted in increased consumption of fossil fuels for electricity generation. The emissions from this combustion have contributed to increasing ambient levels of carbon dioxide in the atmosphere. Many efforts have been made to curb and reduce carbon dioxide emissions in the most efficient manner. The computer process modeling software CHEMCAD was used to model a natural gas combined cycle powerplant for carbon capture and sequestration. Equipment for two proven carbon capture techniques, oxy-combustion and post-combustion amine scrubbing, were modeled. The necessary components modeled included an air separation unit, powerplant, amine scrubbing unit, and a carbon dioxide compression and drying unit. The oxygen concentration in the oxidizer supplied to the powerplant was varied from ambient air, 21%, to nearly pure oxygen, 99.6%. Exhaust gas recirculation was incorporated to maintain a constant combustion temperature. At ambient conditions no air separation unit was necessary and all carbon capture was provided by the amine scrubbing unit. At concentrations ranging from 22 - 99% both oxy-combustion and amine scrubbing techniques are used at inversely varying degrees. At 99.6%, no amine scrubbing unit was necessary. As the oxygen concentration was varied operational parameters were investigated with the goal of identifying optimum operational conditions. Across the varying oxygen concentrations, the First Law efficiency losses ranged from 3.3 - 13.6%. The optimal operational point occurred when ambient air was supplied and exhaust gas recirculation was utilized for flame temperature control. A Second Law efficiency of 52.2% was maximized at an oxygen concentration of 22%. This corresponds to a 2.28% reduction in Second Law efficiency. An exergy analysis of each component identified the air separation unit as the component where the most improvements are possible. At 99% oxygen concentration, the Second Law efficiency of the air separation unit was 3%. Through modeling a natural gas combined cycle powerplant for carbon capture and varying the oxy-combustion characteristics, valuable information was gained in the understanding of operational losses associated with carbon capture.
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.
Analytical modeling and design optimization of piezoelectric bimorph energy harvester
(University of Alabama Libraries, 2010) Zhang, Long; Williams, Keith A.; University of Alabama Tuscaloosa
As wireless sensor networks continue to grow in size and scope, the limited life span of batteries produces an increasingly challenging economic problem, in terms of not only the capital cost of replacing so many batteries, but also the labor costs incurred in performing battery replacement, particularly with sensor nodes in remote or limited-access locations. This growing problem has motivated the development of new technologies for harvesting energy from the ambient environment. Piezoelectric energy harvesters (PEH) are under consideration as a means for converting mechanical energy, specifically vibration energy, to electrical energy, with the goal of realizing completely self-powered sensor systems. There are three primary goals with regards to this study. The first goal is to develop an analytical model for the resonant frequency of a piezoelectric cantilever bimorph (PCB) energy harvester, aiming to study the geometric effects of both the piezoelectric bimorph and the proof mass on the resonant frequency of a PEH. The analytical model is developed using the Rayleigh-Ritz method and Lagrange's equation of motion and is validated by finite element analysis (FEA) and laboratory experiments. It is shown that this analytical model is better at predicting resonant frequencies than a model currently available in the literature. The second goal is the development of an enhanced analytical model for the voltage and power output of the PCB. The modified analytical model is realized using the conservation of energy method and Euler-Bernoulli beam theory. It is compared with a general equivalent spring-mass-damper model and an equivalent electrical circuit model, and validated by the laboratory prototype experiments. The results show that the modified model provides improved prediction of PCB voltage and power output. Simultaneously, finite element analysis on piezoelectric structures using the commercially available software package ANSYS® Multiphysics is also carried out to study the dynamic response of the PCB in terms of both tip displacements and the electrical potentials of the top and bottom electrodes. It is shown that the simulations are quite close to the experimental results, in terms of both peak frequencies and peak amplitudes. The third goal is the design optimization of the PCB energy harvester in order to maximize the power harvesting from the ambient vibration. Three design optimization approaches are carried out, including multi-parameter optimization of the single PCB generator using a genetic algorithm (GA), a band-pass generator design with a group of the PCB generators based on the system transfer function, and the new design features of the PCB generator for consideration of the improvements of the strain energy and the lifetime. The results of the optimized designs are validated through FEA, and the discrepancies between the theoretical derivation and FEA are also analyzed. Other optimal design considerations are also discussed.
Analyzing industrial energy use through ordinary least squares regression models
(University of Alabama Libraries, 2014) Golden, Allyson Katherine; Woodbury, Keith A.; University of Alabama Tuscaloosa
Extensive research has been performed using regression analysis and calibrated simulations to create baseline energy consumption models for residential buildings and commercial institutions. However, few attempts have been made to discuss the applicability of these methodologies to establish baseline energy consumption models for industrial manufacturing facilities. In the few studies of industrial facilities, the presented linear change-point and degree-day regression analyses illustrate ideal cases. It follows that there is a need in the established literature to discuss the methodologies and to determine their applicability for establishing baseline energy consumption models of industrial manufacturing facilities. The thesis determines the effectiveness of simple inverse linear statistical regression models when establishing baseline energy consumption models for industrial manufacturing facilities. Ordinary least squares change-point and degree-day regression methods are used to create baseline energy consumption models for nine different case studies of industrial manufacturing facilities located in the southeastern United States. The influence of ambient dry-bulb temperature and production on total facility energy consumption is observed. The energy consumption behavior of industrial manufacturing facilities is only sometimes sufficiently explained by temperature, production, or a combination of the two variables. This thesis also provides methods for generating baseline energy models that are straightforward and accessible to anyone in the industrial manufacturing community. The methods outlined in this thesis may be easily replicated by anyone that possesses basic spreadsheet software and general knowledge of the relationship between energy consumption and weather, production, or other influential variables. With the help of simple inverse linear regression models, industrial manufacturing facilities may better understand their energy consumption and production behavior, and identify opportunities for energy and cost savings. This thesis study also utilizes change-point and degree-day baseline energy models to disaggregate facility annual energy consumption into separate industrial end-user categories. The baseline energy model provides a suitable and economical alternative to sub-metering individual manufacturing equipment. One case study describes the conjoined use of baseline energy models and facility information gathered during a one-day onsite visit to perform an end-point energy analysis of an injection molding facility conducted by the Alabama Industrial Assessment Center. Applying baseline regression model results to the end-point energy analysis allowed the AIAC to better approximate the annual energy consumption of the facility's HVAC system.
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.
Application of macroscopic elasticity models to predict microstructurally small crack growth
(University of Alabama Libraries, 2018) Cauthen, Tanner; Daniewicz, Steven R.; University of Alabama Tuscaloosa
The need for a more lightweight and structurally stable alloy is evident in industry. Before a specific alloy is put into industrial use, the alloy must be properly tested such that structural integrity is insured. In this study, the microstructurally small crack growth behavior in aluminum and magnesium alloys and its relationship with material microstructure is investigated under fatigue loading. Surfaces of the alloys tested were replicated using a two-part silicon epoxy where the microstructurally small surface cracks were analyzed and measured. The microstructure of the alloys tested was also investigated to see if a correlation between crack growth and microstructure could be found. Fractography and Electron Back Scatter Diffraction (EBSD) were conducted on all three alloys. In addition to the experimental aspects of this study, two linear elastic fracture mechanics models were implemented to see if the trends in crack growth rate could be predicted. The first model, a modified strip-yield model that allowed for plasticity ahead of the crack tip, adequately predicted microstructurally small crack growth for a rolled AZ31 magnesium alloy. The second model, a dislocation distribution theory model (DDM) that allowed for stress intensity factor prediction of a multiply kinked crack in a field of cracks, less than adequately predicted the small crack growth of a rolled AA2XXX and AA7XXX alloy.
As-deposited microstructure and tensile behavior of solid-state additive manufactured Ti–6Al–4V
(University of Alabama Libraries, 2019) Miller, Mary Olivia; Allison, Paul G.; University of Alabama Tuscaloosa
Ti–6Al–4V (Ti64) is the most widely used titanium alloy on the market today due to its high strength-to-weight ratio and excellent corrosion resistance. Because of expensive Ti64 material costs, fusion based additive manufacturing (AM) methods are heavily utilized in the fabrication of Ti64 parts. This research presents the as-deposited properties and microstructure of Ti64 after additive friction stir-deposition (AFS-D): a layer-by-layer solid state AM process that provides the capability to produce fully dense, near-net shape parts from a variety of alloys, including Ti64. Microstructural characteristics of AFS-D Ti64 were determined using Electron Backscatter Diffraction (EBSD) and Energy Dispersive Spectroscopy (EDS). A Vickers hardness test measured the hardness of the AFS-D Ti64 deposition cross section. Quasi-static tensile experiments performed on as-built AFS-D Ti64 samples quantified the strength and ductility, and the results were compared to the data available in the open literature of Ti64 produced by other popular AM methods. In conclusion, the as-deposited AFS-D Ti64 performed as well as or better mechanically than cast and wrought Ti64, with a Vickers hardness of 348 HV and average ultimate tensile strength (UTS) of 1.2 GPa. Compared to other fusion based AM methods, AFS-D performed similarly, while possessing faster deposition rates with a more refined and equiaxed microstructure.

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