Dynamic Modeling of Soft Robotic Dielectric Elastomer Actuator

Dielectric elastomers actuators (DEAs) are among the preferred materials for developing lightweight, high compliance and energy efficient driven mechanisms for soft robots. Simple DEAs consist mostly of a homogeneous elastomeric materials that transduce electrical energy into mechanical deformation by means of electrostatic attraction forces from coated electrodes. Furthermore, stacking multiple single DEAs can escalate the total mechanical displacement performed by the actuator, such is the case of multilayer DEAs. The presented research proposes a model for the dynamical characterization of multilayer DEAs in the mechanical and electrical domain. The analytical model is derived by using free body diagrams and lumped parameters that recreate an analogous system representing the multiphysics dynamics within the DEA. Hyperelasticity in most elastomeric materials is characterized by a nonlinear spring capable of undergoing large deformation; thus, defining the isostatic nonlinear relationship between stress and stretch. The transient response is added by employing the generalize Kelvin-Maxwell elements model of viscoelasticity in parallel with the hyperplastic spring. The electrostatic pressure applied by the electrodes appears as an external mechanical pressure that compress the material; thus, representing the bridge between the electrical and mechanical domain. Moreover, DEAs can be represented as compliant capacitors that change their capacitance as it keeps deforming; consequently, this feature can be used for purposes of self-sensing since there is always a capacitance value that can be mapped into the actual displacement. Therefore, an analytical model of an equivalent circuit of the actuator is also derived to analyze the changes in the capacitance while the actuator is under duty. The models presented analytically are then cross-validated by finite element methods using COMSOL Multiphysics® as the software tool. The results from both models, the analytical and FEM model, were compared by virtually recreating the dynamics of a multilayer DEA with general circular cross section and material parameters from VHB4905 3M commercially available tape. Furthermore, this research takes the general dynamical framework built for DEAs and expand it to model the dynamical system for helical dielectric elastomer actuators (HDEAs) which is a novel configuration of the classical stack that increases the nonlinearity of the system. Finally, this research present a complementary study on enhancing the dielectric permittivity for DEAs, which is an electrical material property that can be optimized to improve the relationship between voltage applied and deformation of the actuator

Lower Limb Knee Exoskeleton

This project is the primary phase to develop a prototype of a lower limb exoskeleton through the implementation of novel hardware and software techniques that will overcome specific issues those current exoskeletons suffer from, such as lack of robust controllability, bulkiness, and actuation performance. At this phase, a knee exoskeleton with a linear actuator has been constructed. It is controlled by using data received from electromyography (EMG) signals. Furthermore, 3D printed parts of the exoskeleton frame have been developed in order to reduce weight and for rapid prototyping. The device is to be designed as an assistive device for a non-handicapped person. The main requirement of the device is to aim more less 20% of the knee joint torque of the average human male (20-35 years) during walking. Furthermore, preliminary studies on electromechanical properties of soft robotic materials have been performed in order to explore their capabilities for this application

Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting

Objective: Seizure forecasting may provide patients with timely warnings to adapt their daily activities and help clinicians deliver more objective, personalized treatments. Although recent work has convincingly demonstrated that seizure risk assessment is in principle possible, these early approaches relied largely on complex, often invasive setups including intracranial electrocorticography, implanted devices, and multichannel electroencephalography, and required patient-specific adaptation or learning to perform optimally, all of which limit translation to broad clinical application. To facilitate broader adaptation of seizure forecasting in clinical practice, noninvasive, easily applicable techniques that reliably assess seizure risk without much prior tuning are crucial. Wristbands that continuously record physiological parameters, including electrodermal activity, body temperature, blood volume pulse, and actigraphy, may afford monitoring of autonomous nervous system function and movement relevant for such a task, hence minimizing potential complications associated with invasive monitoring and avoiding stigma associated with bulky external monitoring devices on the head. Methods: Here, we applied deep learning on multimodal wristband sensor data from 69 patients with epilepsy (total duration > 2311 hours, 452 seizures) to assess its capability to forecast seizures in a statistically significant way. Results: Using a leave-one-subject-out cross-validation approach, we identified better-than-chance predictability in 43% of the patients. Time-matched seizure surrogate data analyses indicated forecasting not to be driven simply by time of day or vigilance state. Prediction performance peaked when all sensor modalities were used, and did not differ between generalized and focal seizure types, but generally increased with the size of the training dataset, indicating potential further improvement with larger datasets in the future. Significance: Collectively, these results show that statistically significant seizure risk assessments are feasible from easy-to-use, noninvasive wearable devices without the need of patient-specific training or parameter optimization

Climate-induced Migration in the MENA Region: Results from the Qualitative Fieldwork

This chapter is based on qualitative focus group and in-depth interview data collected among rural residents and urban migrants in the five focus countries for this study. The chapter documents the relationship between climate change and internal human mobility as seen by the population, as well as some of the other adaptation strategies used by households to cope with a deteriorating climate. Rural residents are clearly aware of climate change. They perceive a shift in climactic conditions that affects their livelihood due to deteriorating agricultural conditions. Among households affected by climate change, migration appears to be more of a strategy of last resort than of first resort, although there are exceptions. For those who migrate to urban areas, obtaining a job as well as a proper dwelling is hard and further hindered by corruption and competition for limited employment opportunities. The obligation to send remittances also puts pressure on migrants. Yet, despite difficulties and pressures, the perceived benefits of migration in terms of the independence and opportunities afforded by urban life remain substantial

Expanding the diversity of mycobacteriophages: Insights into genome architecture and evolution

Mycobacteriophages are viruses that infect mycobacterial hosts such as Mycobacterium smegmatis and Mycobacterium tuberculosis. All mycobacteriophages characterized to date are dsDNA tailed phages, and have either siphoviral or myoviral morphotypes. However, their genetic diversity is considerable, and although sixty-two genomes have been sequenced and comparatively analyzed, these likely represent only a small portion of the diversity of the mycobacteriophage population at large. Here we report the isolation, sequencing and comparative genomic analysis of 18 new mycobacteriophages isolated from geographically distinct locations within the United States. Although no clear correlation between location and genome type can be discerned, these genomes expand our knowledge of mycobacteriophage diversity and enhance our understanding of the roles of mobile elements in viral evolution. Expansion of the number of mycobacteriophages grouped within Cluster A provides insights into the basis of immune specificity in these temperate phages, and we also describe a novel example of apparent immunity theft. The isolation and genomic analysis of bacteriophages by freshman college students provides an example of an authentic research experience for novice scientists. © 2011 Hatfull et al

Effects of Ferroelectric Fillers on Composite Dielectric Elastomer Actuator

Integrating nano- to micro-sized dielectric fillers to elastomer matrices to form dielectric composites is one of the commonly utilized methods to improve the performance of dielectric elastomer actuators (DEAs). Barium titanate (BaTiO3) is among the widely used ferroelectric fillers for this purpose; however, calcium copper titanate CaCu3Ti4O12 (CCTO) has the potential to outperform such conventional fillers. Despite their promising performance, CCTO-based dielectric composites for DEA application are studied to a relatively lower degree. Particularly, the composites are characterized for a comparably small particle loading range, while critical DEA properties such as breakdown strength and nonlinear elasticity are barely addressed in the literature. Thus, in this study, CCTO was paired with polydimethylsiloxane (CH3)3SiO[Si(CH3)2O]nSi(CH3)3 (PDMS), Sylgard 184, to gain a comprehensive understanding of the effects of particle loading and size on the dielectric composite properties important for DEA applications. The dielectric composites’ performance was described through the figures of merit (FOMs) that consider materials’ Young’s modulus, dielectric permittivity, and breakdown strength. The optimum amounts of the ferroelectric filler were determined through the FOMs to maximize composite DEA performance. Lastly, electromechanical testing of the pre-stretched CCTO-composite DEA validated the improved performance over the plain elastomer DEA, with deviations from prediction attributed to the studied composites’ nonlinearity