A novel exoskeleton robotic system was developed to assist stair climbing. This active demonstrator consists of a motor with a cable system, various sensors, and a control system with a power supply. The objective of this preliminary study is a biomechanical evaluation of the novel system to determine its effectiveness in use. For this purpose, three test persons were biomechanically investigated, who performed stair ascents and descents with and without the exoskeleton. Kinematics, kinetics, and muscle activity of the knee extensors were measured. The measured data were biomechanically simulated in order to evaluate the characteristics of joint angles, moments, and reaction forces. The results show that the new exoskeleton assists both the ascent and the descent according to the measured surface electromyography (sEMG) signals, as the knee extensors are relieved by an average of 19.3%. In addition, differences in the interaction between the test persons and the system were found. This could be due to a slightly different operation of the assisting force or to the different influence of the system on the kinematics of the users.

Various types of magnetic nanoparticles have been developed for a range of medical applications and some have the potential to treat diseases. In this paper, a new method for controlling the position of these magnetic particles by changing the magnetic field surrounding them using three Halbach ringsis investigated with the aid of a simulation. The inner ring controls the direction, and the position of the two outer rings can keep the group of particles together. In the simulation, the magnetic field is calculated analytically for each time step and the movement of the particles is simulated by applying the corresponding forces. These result from the manipulable external magnetic field, the magnetic field between the particles, the Lennard-Jones potential to model attractive interactions between the particles and the drag. In the simulation, we show how the particles are manipulated using a Levenberg-Marquard algorithm and develop a closedloop control for the position of the grouped particles by manipulating the magnetic field inside the Halbach rings by changing the angular position of these rings.

Digitale Zwillinge werden in der Betriebsphase einer Maschine oder Anlage für verschiedene Fragestellungen angewendet, bspw. für die Zustandserkennung. Voraussetzung dafür ist, dass der Digitale Zwilling über Modelle des Anlagenverhaltens verfügt, die verschiedene Zustände, einschließlich von Fehlerzustände, adäquat beschreiben. Ein wesentlicher Aspekt dabei ist die Identifikation der Modellparameter. Dieser Beitrag dient der Analyse und Anpassung ausgewählter Optimierungsmethoden (gradientenbasierter Verfahren und ein Partikelschwarmoptimierer) am Beispiel von Modellen für Kompressoren und Turbinenanlagen. Dazu wird mit einem analytischen Ansatz ein Modell einer realen Kompressoranlage erstellt. Um die Qualität der Optimierungsergebnisse vergleichen zu können, wird eine Fehlermetrik eingeführt. Das Verhalten der Optimierer wird unter den folgenden realistischen Fehlerszenarien ermittelt: Fehler in den Modellparametern, Rauschen, initialer Abstand vom Optimum, fehlerhaft Messstellen (ohne und mit Detektion). Zur numerischen Bewertung der Identifizierbarkeit werden zwei Kriterien eingeführt.

This paper presents a simulation system (“patient model”) for intraoperative neuromonitoring (IONM) applied to mastoidectomy. IONM is an electrophysiological method for monitoring the integrity and localization of nerve tracts, which helps the surgeon to avoid injuries and damage to neural risk structures (e.g. facial nerve) during surgery. To use the IONM successfully, the surgeon needs appropriate training and experience. The presented simulation system provides training possibilities in a realistic, cost-efficient and reproducible way. In the simulation system, the position of the probe during training is determined by a magnetic tracking method. Depending on the distance to the virtual nerve, a synthetic electromyogram (EMG) signal is sent to a real neuromonitor. The trainee learns to interpret the output of the neuromonitor. The trainer can choose different training scenarios, such as localization of the nerve, milling or coagulating, using a web application.