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Conventional EEG devices cannot be used in everyday life and
hence, past decade research has been focused on Ear-EEG for mobile,
at-home monitoring for various applications ranging from
emotion detection to sleep monitoring. As the area available for
electrode contact in the ear is limited, the electrode size and location
play a vital role for an Ear-EEG system. In this investigation, we
present a quantitative study of ear-electrodes with two electrode
sizes at different locations in a wet and dry configuration. Electrode
impedance scales inversely with size and ranges from 450 kΩ to
1.29 MΩ for dry and from 22 kΩ to 42 kΩ for wet contact at 10 Hz.
For any size, the location in the ear canal with the lowest impedance
is ELE (Left Ear Superior), presumably due to increased contact
pressure caused by the outer-ear anatomy. The results can be used
to optimize signal pickup and SNR for specific applications. We
demonstrate this by recording sleep spindles during sleep onset
with high quality (5.27 μVrms).
This paper describes the concept of an innovative, interdisciplinary, user-oriented earthquake warning and rapid response system coupled with a structural health monitoring system (SHM), capable to detect structural damages in real time. The novel system is based on interconnected decentralized seismic and structural health monitoring sensors. It is developed and will be exemplarily applied on critical infrastructures in Lower Rhine Region, in particular on a road bridge and within a chemical industrial facility. A communication network is responsible to exchange information between sensors and forward warnings and status reports about infrastructures’health condition to the concerned recipients (e.g., facility operators, local authorities). Safety measures such as emergency shutdowns are activated to mitigate structural damages and damage propagation. Local monitoring systems of the infrastructures are integrated in BIM models. The visualization of sensor data and the graphic representation of the detected damages provide spatial content to sensors data and serve as a useful and effective tool for the decision-making processes after an earthquake in the region under consideration.
Clinical assessment of newly developed sensors is important for ensuring their validity. Comparing recordings of emerging electrocardiography (ECG) systems to a reference ECG system requires accurate synchronization of data from both devices. Current methods can be inefficient and prone to errors. To address this issue, three algorithms are presented to synchronize two ECG time series from different recording systems: Binned R-peak Correlation, R-R Interval Correlation, and Average R-peak Distance. These algorithms reduce ECG data to their cyclic features, mitigating inefficiencies and minimizing discrepancies between different recording systems. We evaluate the performance of these algorithms using high-quality data and then assess their robustness after manipulating the R-peaks. Our results show that R-R Interval Correlation was the most efficient, whereas the Average R-peak Distance and Binned R-peak Correlation were more robust against noisy data.