Multifunctional Sensor Array for User Interaction Based on Dielectric Elastomers with Sputtered Metal Electrodes.

The integration of textile-based sensing and actuation elements has become increasingly important across various fields, driven by the growing demand for smart textiles in healthcare, sports, and wearable electronics. This paper presents the development of a small, smart dielectric elastomer (DE)-based sensing array designed for user control input in applications such as human-machine interaction, virtual object manipulation, and robotics. DE-based sensors are ideal for textile integration due to their flexibility, lightweight nature, and ability to seamlessly conform to surfaces without compromising comfort.

Electrode Impact on the Electrical Breakdown of Dielectric Elastomer Thin Films.

Dielectric Elastomer Actuators (DEAs) enable the realization of energy-efficient and compact actuator systems. DEAs operate at the kilovolt range with typically microampere-level currents and hence minimize thermal losses in comparison to low voltage/high current actuators such as shape memory alloys or solenoids. The main limiting factor for reaching high energy density in high voltage applications is dielectric breakdown.

A Multifunctional Characterization Test Bench for Shape Memory Alloy Micro-Wires-Design, Implementation and Validation.

Most relevant to predicting the behavior of shape-memory-alloy (SMA)-based actuator-sensor applications activated by Joule heating are the electro-mechanical characteristics of the material under consideration. For a comprehensive characterization, a single setup that is able to provide all relevant data and information is desirable. This work covers the design, implementation and validation of such a high-end test bench for the holistic characterization of SMA micro-wires.

Concepts and clinical aspects of active implants for the treatment of bone fractures.

Nonunion is a complication of long bone fractures that leads to disability, morbidity and high costs. Early detection is difficult and treatment through external stimulation and revision surgery is often a lengthy process. Therefore, alternative diagnostic and therapeutic options are currently being explored, including the use of external and internal sensors.

Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves.

Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand.