Knowledge of the mechanical properties and fatigue behavior of thin films is important for the design and reliability of microfabricated devices. This study uses the bulge test to measure the residual stress, Young's modulus, and fracture strength of aluminum nitride (AlN) thin films with different microstructures prepared by sputtering, metalorganic vapor phase epitaxy

Atrial fibrillation (AFib) is the most common cardiac arrhythmia, affecting eventually up to a quarter of the population. The purpose of this small scale clinical study was to validate the usability of MEMS accelerometer based bedsensor for detection of AFib. A Murata accelerometer based ballistocardiogram bedsensor was attached under the hospital bed magnetically and measurement data was recorded from 20 AFib patients and 15 healthy volunteers, mainly females. The recording time was up 30 minutes. The sensor built-in algorithms automatically extracted features such as heart rate (HR), heart rate variability (HRV), relative stroke volume (SVOL), signal strength (SS) and whether the patient is in bed or not. We calculated median values for each feature HR, HRV, SVOL and SS, and investigated whether it is possible to separate AFib from healthy with these features or their combinations. Areas under the curve (AUC) were 0.98 for full length signals and 0.85 for 3 min signal segments using random forest (RF) classifier corresponding to sensitivity and specificity of 100% and 93.3% for full length signals and 90% and 80% for 3 min signals. We conclude, that based on our pilot results, the Murata bedsensor is able to detect AFib, and seems to be a promising technology for long-term monitoring of AFib at home settings as it requires only one-time installation and operational time can be up to years and even tens of years.

Brain-computer interfaces (BCIs) can use data from non-invasive electroencephalogram (EEG) to transform different brain signals into binary code, often aiming to gain control utility of an end-effector (e.g mouse cursor). In the past several years, advances in wearable and immersive technologies have made it possible to integrate EEG with virtual reality (VR) headsets. These advances have enabled a new generation of user studies that help researchers improve understanding of various issues in current VR design (e.g. cybersickness and locomotion). The main challenge for integrating EEG-based BCIs into VR environments is to develop communication architectures that deliver robust, reliable and lossless data flows. Furthermore, user comfort and near real-time interactivity create additional challenges. We conducted two experiments in which a consumer-grade EEG headband (Muse2) was utilized to assess the feasibility of an EEG-based BCI in virtual environments. We first conducted a pilot experiment that consisted of a simple task of object re-scaling inside the VR space using focus values generated from the user’s EEG. The subsequent study experiment consisted of two groups (control and experimental) performing two tasks: telekinesis and teleportation. Our user research study shows the viability of EEG for real-time interactions in non-serious applications such as games. We further suggest that a simplified way of calculating the mean EEG values is adequate for this type of use. We , in addition, discuss the findings to help improve the design of user research studies that deploy similar EEG-based BCIs in VR environments.