We present performance results concerning the validation for anxiety level detection based on trained mathematical models using supervised machine learning techniques. The model training is based on biosignals acquired in a randomized controlled trial. Wearable sensors were used to collect electrocardiogram, electrodermal activity, and respiration from spider-fearful individuals. We designed and applied ten approaches for data labeling considering individual biosignals as well as subjective ratings. Performance results revealed a selection of trained models adapted for two-level (low and high) and three-level (low, medium and high) classification of anxiety using a minimal set of six features. We obtained a remarkable accuracy of 89.8% for the two-level classification and of 74.4% for the three-level classification using a short time window length of ten seconds when applying the approach that uses subjective ratings for data labeling. Bagged Trees proved to be the most suitable classifier type among the classification models studied. The trained models will have a practical impact on the feasibility study of an augmented reality exposure therapy based on a therapeutic game for the treatment of arachnophobia.

The design of safe stimulation protocols for functional electrostimulation requires knowledge of the “maximum reversible charge injection capacity” of the implantable microelectrodes. One of the main difficulties encountered in characterizing such microelectrodes is the calculation of the access voltage V_a. This paper proposes a method to calculate V_a that does not require prior knowledge of the overpotential terms and of the electrolyte (or excitable tissue) resistance, which is an advantage for in vivo electrochemical characterization of microelectrodes. To validate this method, we compare the calculated results with those obtained from conventional methods for characterizing three flexible platinum microelectrodes by cyclic voltammetry and voltage transient measurements. This paper presents the experimental setup, the required instrumentation, and the signal processing.

Animal testing plays a vital role in biomedical research. Stress reduction is important for improving research results and increasing the welfare and the quality of life of laboratory animals. To estimate stress we believe it is of great importance to develop non-invasive techniques for monitoring physiological signals during the transport of laboratory animals, thereby allowing the gathering of information on the transport conditions, and, eventually, the improvement of these conditions. Here, we study the suitability of commercially available electric potential integrated circuit (EPIC) sensors, using both contact and contactless techniques, for monitoring the heart rate and breathing rate of non-restrained, non-sedated laboratory mice. The design has been tested under different scenarios with the aim of checking the plausibility of performing contactless capture of mouse heart activity (ideally with an electrocardiogram). First experimental results are shown.

Depending on their use, electrodes must have a certain size and design so as not to compromise their electrical characteristics. It is fundamental to be aware of all dependences on external factors that vary the electrochemical characteristics of the electrodes. When using implantable electrodes, the maximum charge injection capacity (CIC) is the total amount of charge that can be injected into the tissue in a reversible way. It is fundamental to know the relations between the characteristics of the microelectrode itself and its maximum CIC in order to develop microelectrodes that will be used in biomedical applications. CIC is a very complex measure that depends on many factors: material, size (geometric and effectiveness area), and shape of the implantable microelectrode and long-term behavior, composition, and temperature of the electrolyte. In this paper, our previously proposed measurement setup and automated calculation method are used to characterize a graphene microelectrode and to measure the behavior of a set of microelectrodes that have been developed in the Fraunhofer Institute for Biomedical Engineering (IBMT) labs. We provide an electrochemical evaluation of CIC for these microelectrodes by examining the role of the following variables: pulse width of the stimulation signal, electrode geometry and size, roughness factor, solution, and long-term behavior. We hope the results presented in this paper will be useful for future studies and for the manufacture of advanced implantable microelectrodes.

Purpose: To improve the acceptance and compliance of treatment of amblyopia, the aim of this study was to show that it is feasible to design an electronic frame for context-sensitive liquid crystal glasses, which can measure the state of wear position in a robust manner and detect distinct motion patterns for activity recognition. Methods: Different temple designs with integrated temperature and capacitive sensors were developed to realize the detection of the state of wear position to distinguish three states (correct position/wrong position/glasses taken off). The electronic glasses frame was further designed as a tool for accelerometer data acquisition, which was used for algorithm development for activity classification. For this purpose, training data of 20 voluntary healthy adult subjects (5 females, 15 males) were recorded and a 10-fold cross-validation was computed for classifier selection. In order to perform functional testing of the electronic glasses frame, a proof of concept study was performed in a small group of healthy adults. Four healthy adult subjects (2 females, 2 males) were included to wear the electronic glasses frame and to protocol their activities in their everyday life according to a defined test protocol. Individual and averaged results for the precision of the state of wear position detection and of the activity recognition were calculated. Results: Context-sensitive control algorithms were developed which detected the state of wear position and activity in a proof of concept. The pilot study revealed an average of 91.4% agreement of the detected states of wear position. The activity recognition match was 82.2% when applying an additional filter criterion. Removing the glasses was always detected 100% correctly. Conclusion: The principles investigated are suitable for detecting the glasses’ state of wear position and for recognizing the wearer´s activity in a smart glasses concept.

In recent years, the development of implantable electronics has been driven by the motivation to expand their field of application. The main intention is to implement advanced functionalities while increasing the degree of miniaturization and maintaining reliability. The intrinsic nonlinear properties of the electronic components, to be used anyway, could be utilized to resolve this issue. To master the implementation of functionalities in implantable electronics using the nonlinear properties of its electronic components, simulation models are of utmost importance. In this paper, we present a simulation model that is optimized in terms of consistency, computing time and memory consumption. Three circuit topologies of nonlinear capacitors, including hysteresis losses, are investigated. An inductively coupled measurement setup was realized to validate the calculations. The best results were obtained using the Trapezoid method in ANSYS with a constant step size and a resolution of 500 k points and using the Adams method in Mathcad with a resolution of 50 k points. An inductive coupling factor between 7% and 10% leads to a significant improvement in consistency compared to lower coupling factors. Finally, our results indicate that the nonlinear properties of the voltage rectifier capacitor can be neglected since these do not significantly affect the simulation results.