Mechanical Engineering

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DIRECT LASER WRITE PROCESSES FOR SPIDER INSPIRED MICROHYDRAULICS AND MULTI-SCALE LIQUID METAL DEVICES
(2023) Smith, Gabriel Lewis; Bergbreiter, Sarah; Sochol, Ryan; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Direct Laser Write (DLW) through two-photon polymerization (2PP) empowers us to delveinto the realm of genuine three-dimensional design complexity for microsystems, enabling features smaller than a single micrometer. This dissertation develops two novel fabrication processes that leverage DLW for functional fluidic microsystems. In the first process, we are inspired by arachnids that use internal hemolymph pressure to actuate extension in one or more of their leg joints. The inherent large foot displacement-to-body length ratio that arachnids can achieve through hydraulics relative to muscle-based actuators is both energy and volumetrically efficient. Until recent advances in nano/microscale 3-D printing with 2PP, the physical realization of synthetic complex ‘soft’ joints would have been impossible to replicate and fill with a hydraulic fluid into a sealed sub-millimeter system. This dissertation demonstrates the smallest scale 3D-printed hydraulic actuator 4.9 × 10^−4 mm^3 by more than an order of magnitude. The use of stiff 2PP polymers with micron-scale dimensions enable compliant membranes similar to exoskeletons seen in nature without the requirement for low-modulus materials. The bio-inspired system is designed to mimic similar hydraulic pressure-activated mechanisms in arachnid joints utilized for large displacement motions relative to body length. Using variations on this actuator design, we demonstrate the ability to transmit forces with relatively large magnitudes (milliNewtons) in 3D space, as well as the ability to direct motion that is useful towards microrobotics and medical applications. Microscale hydraulic actuation provides a promising approach to the transmission of large forces and 3D motions at small scales, previously unattainable in wafer-level 2D microelecromechanical systems (MEMS). The second fabrication process focuses on incorporating functionality through the use of liquid metals in 3D DLW structures. Room temperature eutectic Gallium Indium (eGaIn)- based liquid metal devices with stretchable, conductive, and reconfigurable behavior show great promise across many areas of technology, including robotics, communications, and medicine. Microfluidics provide one means of creating eGaIn devices and circuits, but these devices are typically limited to larger feature sizes. Developments in 3D printing via DLW have enabled sub-100 µm complex microfluidic devices, though interfacing microfluidic devices manufactured with DLW to larger millimeter-scale systems is difficult. The reduced channel diameter creates challenges for removing resist from the channels, filling microchannels with eGaIn, and electrically integrating them to larger channels or other circuitry. These challenges have prevented microscale liquid metal devices from being used more widely. In this dissertation, we demonstrate a facile, low-cost multiscale process for printing DLW microchannels and devices onto centimeter-scale custom fluidic channel substrates fabricated via stereolithography (SLA). This work demonstrates a robust interface between the two independently printed materials and greatly simplifies the filling of eGaIn microfluidic channels down to 50 µm in diameter, with the potential to achieve even smaller feature sizes of liquid metals. This work also demonstrates eGaIn coils with resistance of 43-770 mΩ and inductance of 2-4 nH. As a result, this process empowers us to manufacture interfaces that are not only low-temperature but also conductive and flexible. These interfaces find their application in connecting with sensors, actuators, and integrated circuits, thereby opening new avenues in the field of 3D electronics. Furthermore, our approach extends the lower limits of size-dependent properties for passive electronic components like resistors, capacitors, and inductors crafted from liquid metal, expanding the frontiers of possibilities in miniature electronic design.
PREDICTION AND CLOSED-LOOP CONTROL OF BLOOD PRESSURE FOR HEMORRHAGE RESUSCITATION
(2023) Hohenhaus, Drew Xavier; Hahn, Jin-Oh; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Hemorrhage is responsible for a large percentage of mortality worldwide and the majority of fatalities on the battlefield. Resuscitation procedures for hemorrhage trauma patients are critical for their recovery. Currently, during resuscitation, physicians manually monitor blood pressure and use intuition to determine when fluid should be administered and how much. Due to factors such as exhaustion, distraction, and inexperience of the physician, this method has often been reported as fallible. This thesis proposes two methods to assist in automating hemorrhage resuscitation. The first is a blood pressure prediction algorithm for decision support systems. The algorithm individualizes itself to different subjects using extended Kalman filtering (EKF), to account for high inter-subject variability, before accurately forecasting future blood pressure. The second method is an observer-based feedback controller which regulates blood pressure from a hypotensive state back to a “healthy” setpoint. The controller was designed using linear matrix inequality (LMI) techniques to ensure it was absolutely stable, which let a portion of the hemodynamic plant model remain unspecified and allowed for performance over a range of physiologies. Both strategies were evaluated in-silico on a cohort of 100 virtual patients generated from an experimental dataset. The prediction algorithm showed accuracy superior to conventional assumptions. The controller tracked the given setpoint with an accuracy and performance comparable to more complex adaptive methods. Further work, with respect to the prediction algorithm, includes developing it into a full decision-support system and incorporating disturbance rejecting components to account for common issues such as rebleed. The controller’s performance deteriorates for high-speed applications, suggesting further study is required to increase its situational flexibility.
MATHEMATICAL MODELS AND NOVEL BIOMARKERS TOWARD OPTIMIZATION OF BURN INJURY RESUSCITATION
(2022) Arabidarrehdor, Ghazal; Hahn, Jin-Oh; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Extensive burn injury is not only devastating but also a significant challenge for healthcare providers. Following a chain of inflammatory responses post-burn, significant amounts of plasma shift from the vascular compartment into the tissues, simultaneously posing the risks of hypovolemic shock and edema. Standard burn resuscitation protocols aim to replace the lost blood volume while not exacerbating the edema through hourly-titrated intravenous fluid infusion. Due to the significant variability in treatment efficacy, there is a substantial ongoing effort to optimize and individualize the burn resuscitation protocols. In this work, we aim to contribute to this effort by (i) developing a platform for the virtual evaluation of burn resuscitation protocols and (ii) identifying biomarkers to guide fluid resuscitation effectively. The first part of this work presents a mathematical model of burn injury and resuscitation, which can be used for the development and non-clinical testing of burn resuscitation protocols and algorithms, as well as to garner knowledge and intuition into this complex pathophysiology. Our mathematical model consists of a multi-compartmental model of blood volume kinetics, a hybrid mechanistic-phenomenological model of kidney function, and novel lumped-parameter models of burn-induced perturbations in volume kinetics and renal function. We examined our mathematical model’s prediction accuracy and reliability using a rich dataset from 16 sheep with extensive burn injuries and clinical data from 233 real-world burn patients. The second part of this work presents the expansion of the mathematical model to incorporate the cardiovascular and renin-angiotensin-aldosterone systems, as well as detailed descriptions of the kidney’s mechanisms, particularly regarding its blood volume and blood pressure regulation roles. This expansion was motivated by the importance of cardiovascular monitoring in the critical care of burn injury patients. We trained and validated the expanded mathematical model for three species: nine sheep subjects and 15 swine subjects with rich cardiovascular and volume kinetics data, and 233 human subjects with demographic and urinary output (UO) data. To the best of our knowledge, our mathematical model may be the first of its kind which is extensively validated for use as a digital twin to replicate realistic burn patients and replace standard large animal pre-clinical testing of burn resuscitation protocols. The third part of this work presents the identification of biomarkers capable of guiding, optimizing, and individualizing burn resuscitation. The UO, the most common endpoint used to titrate burn resuscitation fluid doses, has many limitations as a single variable. Hence, this work aimed to find convenient and reliable biomarkers from arterial blood pressure (ABP) waveform to complement UO in guiding burn resuscitation. Pulse pressure variation (PPV), systolic pressure variation (SPV), and stroke volume variation (SVV) are dynamic indices derived from ABP that have shown promise in hemorrhage resuscitation but are not investigated for different resuscitation paradigms for burn injury. We observed the longitudinal behavior of PPV, SPV, and SVV for 21 porcine subjects with 40% burn injury, which were each either under-resuscitated, adequately resuscitated, or deliberately over-resuscitated. We investigated the features' potential in tracking reference cardiac output (CO) and stroke volume (SV) via linear regression and correlation analysis. PPV, SPV, and SVV showed plausible and statistically different trends for different paradigms. While they performed just as well as UO in tracking CO and SV, their inherent advantage of being available in real-time and their disagreement with UO in determining the subject status suggest that they may potentially complement UO in the hemodynamic assessment of burn patients.
A FRAMEWORK FOR CREDIBILITY ASSESSMENT OF SUBJECT-SPECIFIC PHYSIOLOGICAL MODELS
(2022) Parvinian, Bahram; Hahn, Jin-Oh; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Physiological closed-loop controllers and decision support systems are medical devices that enable some degree of automation to meet the needs of patients in resource-limited environments such as critical care and surgical units. Traditional methods of safety and effectiveness evidence generation such as pre-clinical animal and human clinical studies are cost prohibitive and may not fully capture different performance attributes of such complex safety-criticalsystems primarily due to subject variability. In silico studies using subject-specific physiological models (SSPMs) may provide a versatile platform to generate pre-clinical and clinical safety evidence for medical devices and help reduce the size and scope of animal studies and/or clinical trials. To achieve such a goal, the credibility of the SSPMs must be established for the purpose it is intended to serve. While in the past decades significant research has been dedicated towards development oftools and methods for development and evaluation of SSPMs, adoption of such models remains limited, partly due to lack of trust in SSPMs for safety-critical applications. This may be due to a lack of a cohesive and disciplined credibility assessment framework for SSPMs. In this dissertation a novel framework is proposed for credibility assessment of SSPMs. The framework combines various credibility activities in a unified manner to avoid or reduce resource intensive steps, effectively identify model or data limitations, provide direction as to how to address potential model weaknesses, and provide much needed transparency in the model evaluation process to the decision-makers. To identify various credibility activities, the framework is informed by an extensive literature review of more mature modeling spaces focusing on non- SSPMs as well as a literature review identifying gaps in the published work related to SSPMs. The utility of the proposed framework is successfully demonstrated by its application towards credibility assessment of a CO2 ventilatory gas exchange model intended to predict physiological parameters, and a blood volume kinetic model intended to predict changes in blood volume inresponse to fluid resuscitation and hemorrhage. The proposed framework facilitates development of more reliable SSPMs and will result in increased adoption of such models to be used for evaluation of safety-critical medical devices such as Clinical Decision Support (CDS) and Physiological Closed-Loop Controlled (PCLC) systems.
BALLISTOCARDIOGRAPHY: MATHEMATICAL MODELING, ANALYSIS, AND APPLICATION TO CARDIOVASCULAR HEALTH MONITORING
(2022) Mousavi, Azin Sadat; Hahn, Jin-Oh; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The main goal of this dissertation is to improve the early detection and management of cardiovascular (CV) disease by developing novel ultra-convenient CV health and risk predictor monitoring techniques based on a physiological signal called ballistocardiogram (BCG). BCG is the recording of heart-induced body movements. It has great potential to enable ultra-convenient CV health monitoring due to its close association with cardiac functions and its amenity for convenient measurement (i.e., measurement devices such as weighing scales and wearables). Nonetheless, the shortage of physical understanding of the BCG is a serious challenge that has hampered its effective use in CV health and risk assessment. The scope of this dissertation can be explained under three themes: (i) physics-based modeling of BCG, (ii) BCG recording, and (iii) challenges in wearable BCG-based cuffless blood pressure monitoring. In the first part of the dissertation, a closed-form physics-based model is developed to estimate BCG from a single blood pressure waveform. The feasibility of this model in the estimation of CV risk predictors is studied. This model is inspired by our team’s prior work hypothesizing that the main mechanism for the genesis of the head-to-foot BCG is the pressure gradients in the ascending and descending aorta (the major artery in the body). In addition, a systematic BCG feature selection approach is introduced leveraging the developed closed-form BCG model. This model-based approach is superior to previous ad-hoc feature selection techniques in that it incorporates physiological knowledge of the arterial system and unlike ad-hoc approaches which are data specific its findings can be generalized to different independent datasets. BCG waveforms recorded with different sensors and devices have morphological differences. Therefore, the next part of this work is dedicated to the study of different BCG recording devices and the construction of a BCG measurement apparatus that enables the recording of true BCG (as estimated in the mathematical model). The efficacy of the BCG recording apparatus in measuring BCG is shown in two human and animal experiments. Finally, BCG can enable cuff-less blood pressure (BP) tracking by virtue of two perks. It can easily be instrumented using wearables and it can be used as a proximal timing reference to calculate pulse transit time (PTT) which is the basis of the most common technique for cuff-less BP tracking. However, most wearable BCG-based studies for cuff-less BP monitoring have resorted to only one posture (standing with hands placed on the sides). Therefore, in this work, the effect of posture on wrist BCG-PPG PTT is investigated. This work reveals the posture-induced changes in PTT in-depth for the first time, by invoking and quantifying the effect of possible physical mechanisms responsible for such changes.
INFERENCE-BASED MODELING, MONITORING, AND CONTROL ALGORITHMS FOR AUTONOMOUS MEDICAL CARE
(2022) Tivay, Ali; Hahn, Jin-Oh; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Autonomous medical care systems are relatively recent developments in biomedical research that aim to leverage the vigilance, precision, and processing power of computers to assist (or replace) humans in providing medical care to patients. Indeed, past research has demonstrated initial promise for autonomous medical care in applications related to anesthesia, hemodynamic management, and diabetes management, to name a few. However, many of these technologies yet do not exhibit the maturity necessary for widespread real-world adoption and regulatory approval. This can be attributed, in part, to several outstanding challenges associated with the design and development of algorithms that interact with physiological processes. Ideally, an autonomous medical care system should be equipped to exhibit (i) transparent behavior, where the system’s perceptions, reasoning, and decisions are human-interpretable; (ii) context-aware behavior, where the system is capable of remaining mindful of contextual and peripheral information in addition to its primary goal; (iii) coordinated behavior, where the system can coordinate multiple actions in synergistic ways to best achieve multiple objectives; (iv) adaptable behavior, where the system is equipped to identify and adapt to variabilities that exist within and across different patients; and (v) uncertainty-aware behavior, where the system can handle imperfect measurements, quantify the uncertainties that arise as a result, and incorporate them into its decisions. As these desires and challenges are specific to autonomous medical care applications and not fully explored in past research in this area, this dissertation presents a sequence of methodologies to model, monitor, and control a physiological process with special emphasis on addressing these challenges. For this purpose, first, a collective variational inference (C-VI) method is presented that facilitates the creation of personalized and generative physiological models from low-information and heterogeneous datasets. The generative physiological model is of special importance for the purposes of this work, as it encodes physiological knowledge by reproducing the patterned randomness that is observed in physiological datasets. Second, a population-informed particle filtering (PIPF) method is presented that fuses the information encoded in the generative model with real-time clinical data to form perceptions of a patient’s states, characteristics, and events. Third, a population-informed variational control (PIVC) method is presented that leverages the generative model, the perceptions of the PIPF algorithm, and user-defined definitions of actions and rewards in order to search for optimal courses of treatment for a patient. These methods together form a physiological decision-support and closed-loop control (PCLC) framework that is intended to facilitate the desirable behaviors sought in the motivations of this work. The performance, merits, and limitations of this framework are analyzed and discussed based on clinically-important case studies on fluid resuscitation for hemodynamic management.
AUTOMATED MEDICATION INFUSION SYSTEM DESIGN
(2019) Jin, Xin; Hahn, Jin-Oh; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Automated infusion of medications will be increasingly deployed in patient care as a means to deliver high-quality and continuous monitoring and therapy, and also to alleviate the excessive workload imposed on the clinicians. Therefore, a well-designed automated medication infusion system is an attractive alternative to today’s manual treatment requiring caregiver’s interventions. However, it also presents numerous challenges: 1) Significant inter- and intra-patient variability; 2) Complexity of medication infusion model; 3) Complexity of interaction of multiple medications; 4) Difficulty in coordination of medical targets. So the following approaches are proposed to address the various challenges: First, to deal with the large degree of individual patient variability, an adaptive controller was designed. This is because robust controllers which have fixed parameters might be difficult to offer decent behavior for all patients. Secondly, since classical adaptive controllers can only be applied to linearly parametrized models while even the infusion model of single drug is highly nonlinear and complex, a single-input single-output (SISO) semi-adaptive control approach which only adapt can adapt model parameters having a large impact on the model’s fidelity was introduced. Thirdly, the complicated interaction of multiple medications makes the adaptive controller for two medications even more difficult to design. So a model for two interacting dose responses was constructed and linearized at one operation point. Then the SISO semi-adaptive controller was extended to a two-input two-output case. However, this controller is only designed at one operating point. Therefore, based on two models associated with two distinct operating regimes, a two-model switching control technique was developed and combined with the semi-adaptive controller. Fourthly, we presented a coordinate mechanism to deal with the medical targets setting problem. In real clinical scenarios, the reference targets are empirically specified by caregivers, which are not always achievable in all patients. Therefore, our proposed coordinate mechanism can recursively adjusts the reference targets based on the estimated dose-response relationship of a patient. Lastly, we conducted some SISO control experiments on animals. Based on the experiments, we made some further improvements to the proposed controller.
Physics-Based Model-Guided Machine Learning Analysis of Wrist Ballistocardiography for Cuff-Less Blood Pressure Monitoring
(2019) Yousefian, Peyman; Hahn, Jin Oh; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Cuff-less blood pressure (BP) monitoring technology is being widely pursued today. In this research we investigated the wrist ballistocardiogram (BCG) as a limb BCG, to develop a scientific basis to use the limb BCG to for cuff-less BP monitoring. In our study, we pursue two alternative approaches to the use of wrist BCG signal for BP monitoring: (1) use of the wrist BCG as proximal timing in pulse transit time (PTT) based methods; (2) use of wrist BCG wave features for BP monitoring. In this regard, the physics-based model is developed to elucidate the mechanism responsible for the generation of the BCG signal at the body’s extremity limb locations. The developed and experimentally validated mathematical model can predict the limb BCG in responses to the arterial BP waves in the aorta. The model suggests that the limb BCG waveform reveals the timings and amplitudes associated with the aortic BP waves and it exhibits meaningful morphological changes in response to the alterations in the CV risk predictors. Such understanding combined with machine learning techniques has helped us to extract viable features, and construct predictive models that can estimate BP. The findings of this study show that limb BCG has the potential to realize convenient cuff-less BP monitoring. First, it is a strong candidate to extract the proximal timing for PTT based methods. Second, BCG wave features are associated with BP and it could be used for BP monitoring. Third, we can combine the PTT with BCG wave features to achieve more comprehensive prediction models with superior performance.
SYSTEM IDENTIFICATION AND DE-CONVOLUTION OF A CLASS OF MULTI-CHANNEL WAVE PROPAGATION SYSTEMS FOR UNOBTRUSIVE CARDIOVASCULAR HEALTH MONITORING
(2019) Ghasemi, Zahra; Hahn, Jin-Oh; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The main goal of this thesis is to improve the cardiovascular health monitoring by developing a novel model-based blind system identification approach. This research lies on the core idea that the central aortic blood pressure (BP) waveform can be estimated from as few as two non-invasive circulatory signals. To achieve this goal, first, we formulated a physiological model for the class of multi-channel systems with non-invasive BP measurements and expressed it as a blind system identification problem. We verified this model for estimating the central blood pressure waveform from pulse volume records (PVR) signals from arm and leg, collected from 10 human subjects. The results showed that the proposed approach could estimate central aortic blood pressure waveform accurately. The average root-mean-squared error associated with the central aortic blood pressure waveform was 4.1 mmHg while the average errors associated with central aortic systolic and pulse pressures were 2.4 mmHg and 2.0 mmHg respectively. Afterward, we compared this method with a population-based technique to calculate cardiovascular risk predictors. First, we used the same approach to estimate the central blood pressure waveform from two non-invasive peripheral waveforms and then, calculated cardiovascular risk predictors. Experimental results obtained from 164 human subjects with a wide blood pressure range showed that this approach could estimate cardiovascular risk predictors accurately. Further analysis showed that the suggested approach outperformed a generalized transfer function regardless of the degree of pulse pressure amplification, but especially in high and low amplification ranges. Finally, a new closed-loop approach to input de-convolution in coprime multi-channel systems based on state estimation techniques is proposed. This approach is based on the idea that the unknown input signal in a multi-channel system may be regarded as a state variable to be estimated from multiple output signals of the system. The validity and potential of the approach were illustrated using the clinically significant case study of estimating central aortic BP waveform from two non-invasively peripheral arterial pulse waveforms. The proposed algorithm could reduce the root-mean-squared error associated with the central aortic blood pressure by up to 27.5% and 28.8% relative to two conventional central aortic blood pressure estimation techniques: open-loop inverse filtering and peripheral arterial pulse waveforms scaled to central aortic diastolic and mean pressures.
A Control-Theoretic Model of Hemodynamic Responses to Blood Volume Perturbation
(2018) Lo, Alex Kai-Yuan; Hahn, Jin-Oh; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
This thesis presents a mathematical model to reproduce hemodynamic responses of different endpoints to the blood volume perturbation in circulation system. The proposed model includes three sub-models, which are a control-theoretic model relating blood volume response to blood volume perturbation, a physiologic-based model relating cardiac output response to blood volume perturbation, and a control-theoretic model relating mean arterial pressure response to cardiac output perturbation. Two unique characteristics of this hemodynamic model are that the model can reproduce responses accurately even with its simplicity, and can be easily understood by control engineers because of its physiological transparency. With these two advantages, closed-loop resuscitation controller evaluation can be performed in model-based approach instead of evaluating results from animal studies, which are relatively costly and time-consuming. In this thesis, the hemodynamic model was examined and evaluated by using experimental dataset collected from 11 animals. The results of system identification analysis, in-silico evaluation and parametric sensitivity analysis showed that the hemodynamic model may faithfully serve as a evaluation basis for the closed-loop resuscitation controllers.

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