In our fast-changing world, human-machine interfaces (HMIs) are of ever-increasing importance. Among the most ubiquitous examples are touchscreens that most people are familiar with from their smartphones. The quality of such an HMI can be improved by adding haptic feedback-an imitation of using mechanical buttons-to the touchscreen. Thin-film actuators on the basis of electro-mechanically active polymers (EAPs), with the electroactive material sandwiched between two compliant electrodes, offer a promising technology for haptic surfaces. In thin-film technology, the thickness and the number of stacked layers of the electroactive dielectric are key parameters for tuning a system. Therefore, we have experimentally investigated the influence of the thickness of a single EAP layer on the electrical and the electro-mechanical performance of the transducer. In order to achieve high electro-mechanical actuator outputs, we have employed relaxor-ferroelectric ter-fluoropolymers that can be screen-printed. By means of a model-based approach, we have also directly compared single- and multi-layer actuators, thus providing guidelines for optimized transducer configurations with respect to the system requirements of haptic applications for which the operation frequency is of particular importance.
A theory for diffusivity estimation for spatially extended activator-inhibitor dynamics modeling the evolution of intracellular signaling networks is developed in the mathematical framework of stochastic reaction-diffusion systems. In order to account for model uncertainties, we extend the results for parameter estimation for semilinear stochastic partial differential equations, as developed in Pasemann and Stannat (Electron J Stat 14(1):547-579, 2020), to the problem of joint estimation of diffusivity and parametrized reaction terms. Our theoretical findings are applied to the estimation of effective diffusivity of signaling components contributing to intracellular dynamics of the actin cytoskeleton in the model organism Dictyostelium discoideum.
SensorHub
(2022)
Observational studies are an important tool for determining whether the findings from controlled experiments can be transferred into scenarios that are closer to subjects' real-life circumstances. A rigorous approach to observational studies involves collecting data from different sensors to comprehensively capture the situation of the subject. However, this leads to technical difficulties especially if the sensors are from different manufacturers, as multiple data collection tools have to run simultaneously. We present SensorHub, a system that can collect data from various wearable devices from different manufacturers, such as inertial measurement units, portable electrocardiographs, portable electroencephalographs, portable photoplethysmographs, and sensors for electrodermal activity. Additionally, our tool offers the possibility to include ecological momentary assessments (EMAs) in studies. Hence, SensorHub enables multimodal sensor data collection under real-world conditions and allows direct user feedback to be collected through questionnaires, enabling studies at home. In a first study with 11 participants, we successfully used SensorHub to record multiple signals with different devices and collected additional information with the help of EMAs. In addition, we evaluated SensorHub's technical capabilities in several trials with up to 21 participants recording simultaneously using multiple sensors with sampling frequencies as high as 1000 Hz. We could show that although there is a theoretical limitation to the transmissible data rate, in practice this limitation is not an issue and data loss is rare. We conclude that with modern communication protocols and with the increasingly powerful smartphones and wearables, a system like our SensorHub establishes an interoperability framework to adequately combine consumer-grade sensing hardware which enables observational studies in real life.