Volume 72, Issue 5 - Special Issue: AUTOMED; Guest Editors: Philipp Rostalski, Thomas Schanze and Thomas Seel

Bruxism is a parafunctional oral behavior that affects a large percentage of the population. Bruxism is a risk factor for temporomandibular disorders. A gold standard is still lacking for assessing bruxism while awake, whereas for sleep bruxism, polysomnography with audio and video recording is the gold standard. Wearable devices, particularly those that detect sound (hearables), are cost-effective and convenient and could fill the gap. With this systematic literature review of Livivo and PubMed, extended by individual Google Scholar searches, we aimed to assess the potential of wearable devices that use sound as a biomarker for detecting bruxism. In summary, sounds originating from oral behaviors can be recorded from the ear, and hearables have the potential to detect bruxism-like events.

Stress-induced hyperglycemia and high glycemic variability are common in intensive care patients. Several clinical studies show the benefits of tight blood glucose control, including lower mortality. This article presents an algorithm for blood glucose control in the intensive care unit. An Unscented Kalman Filter is developed to estimate the glucose metabolism state and time-varying insulin sensitivity from blood glucose measurements. Gaussian Processes are used to predict future insulin sensitivity changes based on previous measurements. A model predictive controller is designed to estimate optimal insulin infusion based on current state, predicted insulin sensitivity and planned nutrition. The developed control algorithm allows individualized blood glucose control with reduced glycemic variability and reduced risk of hypoglycemia in the intensive care unit.

Feature extraction from an recorded surface electromyography (sEMG) signal plays an important role in identifying and quantifying the characteristics of muscle activities. These features can be used for various applications like muscle function assessment, muscle fatigue detection, etc. Common features extracted from sEMG signal are time-domain or frequency-domain features. However, features which are sensitive to uncertainties in the signal like noise, movement artifacts, and outliers should be avoided. Autocorrelation function (ACF), which is a measure of similarity between a signal and its time delayed version, is considered in this work as a feature to overcome the impact of noise, artifacts, and outliers. An artificial neural network (ANN) is developed to differentiate between fatigue and non-fatigue conditions using the calculated ACF from sEMG segments. The performance of an ANN model that can be adapted by means of various regularization methods was investigated. The proposed ANN model achieved an accuracy of about 97.62 %, a precision of about 95.50 % and a sensitivity of about 100 % in the classification of fatigue and non-fatigue sEMG segments, outperforming k -means and linear support vector machine approaches that served as references.

Expiratory flow limitation (EFL) is an often unrecognized clinical condition with a multitude of negative implications. A mathematical EFL model is proposed to detect flow limitations automatically. The EFL model is a switching one-compartment lung mechanics model with a volume-dependent airway resistance to simulate the dynamic behavior during expiration. The EFL detection is based on a breath-by-breath model parameter identification and validated on clinical data of mechanically ventilated patients. In the severe flow limitation group 93.9 % ± 5 % and in the no limitation group 10.2 % ± 13.7 % of the breaths are detected as EFL. Based on the high detection rate of EFL, these results support the usefulness of the EFL detection. It is a first step toward an automated detection of EFL in clinical applications and may help to reduce underdiagnosis of EFL.

This article presents a hardware-in-the-loop system that can simulate a patient’s physiological responses to mechanical ventilation. The system includes a hardware platform with a mechatronic lung that can physically simulate the respiratory mechanics. A computational patient model replicates the pressure/volume behaviour of the lungs and the impaired gas exchange. Based on current ventilator settings, the model calculates signal curves, which are then transmitted to the physically existing sensors. This enables the test bench to reproduce the pressure/volume behaviour of the lungs and the gas exchange of a simulated patient on mechanical ventilation. In the future, the hardware-in-the-loop system could play an important role in testing and validating highly automated functions in mechanical ventilation and represent an alternative to animal testing.

Highly compliant Pneumatic Soft Actuators (PSAs) have the potential to perform challenging tasks in a broad range of applications that require shape-adaptive capabilities. Achieving accurate tracking control for such actuators with complex geometries and material compositions typically involves many time-consuming and laborious engineering steps. In this work, we propose a data-driven learning-based control approach to address reference tracking tasks, incorporating self-adaptation in situ . We utilize a short interaction maneuver, recorded a priori , to collect the quasi-static data affected by severe hysteresis. Besides a linear feedback controller, we use two Gaussian process models to predict the feedforward control input to compensate for the nonlinearity in a one-shot learning setting. The proposed control approach demonstrates accurate tracking performance even under realistic varying configurations, such as alterations in mass and orientation, without any parameter tuning. Notably, training was achieved with only 25–50 s of experimental interaction, which emphasizes the plug-and-play capabilities in diverse real-world applications.

Vestibular Stimulation (VS) has been shown to positively affect various autonomic body functions, including sleep. In the past, VS was often investigated using large and complex rocking beds that would only allow for short intervention periods in constrained lab settings. In this work, we present an overview of the mechanics, kinematics, dynamics, and tuning of our latest rocking bed, the Somnomat Casa. Its compact dimensions, comparable to a standard single bed, its connectivity, and easy usability, allow for prolonged studies in home settings investigating the effects of VS during sleep. In a first six-month study with a 12-year-old boy suffering from primary mitochondrial disease with an associated severe sleep disorder, we observed significant improvements in sleep duration (+25 %) and quality of life, as well as a 75 % reduction in caregiving interactions and a 40 % reduction in overall caregiving time. Based on these promising findings, we are currently testing the Somnomat Casa with various patient groups for multiple months each, including elderly with insomnia, children with sleep disorders, Parkinson’s disease patients, and individuals with post-stroke insomnia.

In recent years, robotic arm exoskeletons have emerged as promising tools, finding widespread application in the rehabilitation of neurological disorders and as assistive devices for everyday activities, even alleviating the physical strain on labor-intensive tasks. Despite the growing prominence of exoskeletons in everyday life, a notable knowledge gap exists in the availability of open-source platforms for classroom-ready usage in educational settings. To address this deficiency, we introduce an open-source educational exoskeleton platform aimed at Science, Technology, Engineering, and Mathematics (STEM) education. This platform represents an enhancement of the commercial EduExo Pro by AUXIVO, tailored to serve as an educational resource for control engineering and biomedical engineering courses.

Gain in knowledge in experimental fields of research is mostly achieved using commercial hardware platforms. The linked costs however can limit a broad access by scientists and in the specific case of mechanical ventilation the pandemic demonstrated that further and collective research is essential and necessary. This contribution presents the modular design and implementation of a research platform which is based on STM32 Nucleo boards and Simulink ® . The hardware required such as the pneumatic circuit are covered as well as the software architecture in which clinician related settings are processed for control references. The core functionality of mandatory volume controlled (VCV) and pressure controlled ventilation (PCV) while ensuring a positive end-expiratory pressure for the patient ( PEEP ) $(\text{PEEP})$ is evaluated and the foundational software models are published in a repository.

This paper reports on the design of the GUI component of the PV1000, a fully-functional, cost-efficient pandemic ventilator developed during the COVID-19 pandemic. Our main objective in designing the user interface was to expose the rich features of the ventilator, which are on-par with many commercial medical devices, through an interface that is simple to use even by medical staff with little prior experience with ventilators. We present an agile development process which is intended to address the fast changing and potentially unclear requirements of a pandemic. This is to address the specific situation where domain experts as well as specific hardware have shortages, resulting in recurring changes in requirements. This occurs in conjunction with the collaboration of developers, not all of whom are familiar with the development of medical devices. Despite the pandemic situation, we have made a deliberate decision to use existing software engineering tools such as continuous integration. Additionally, we are employing a multi-platform development framework in order to utilise the resulting advantages, for example, if there are changes in requirements. We present and discuss the system, hardware and software architecture of the components that allow users to interact with the device. The focus here is on the dynamic generation of parts of the software architecture, which enables rapid adaptation to changing conditions on the part of the software. The user interface is evaluated through a user survey, where the majority of participants describe the interface as clear and intuitive. The entire ventilator was evaluated in one animal trial and fortunately never had to be used on humans. The next step is to convert the setup into an open source project that can be used as a research platform for ventilators.