Evaluation of Lateral Radar Positioning for Vital Sign Monitoring: An Empirical Study.

Vital sign monitoring is dominated by precise but costly contact-based sensors. Contactless devices such as radars provide a promising alternative. In this article, the effects of lateral radar positions on breathing and heartbeat extraction are evaluated based on a sleep study. A lateral radar position is a radar placement from which multiple human body zones are mapped onto different radar range sections. These body zones can be used to extract breathing and heartbeat motions independently from one another via these different range sections. Radars were positioned above the bed as a conventional approach and on a bedside table as well as at the foot end of the bed as lateral positions. These positions were evaluated based on six nights of sleep collected from healthy volunteers with polysomnography (PSG) as a reference system. For breathing extraction, comparable results were observed for all three radar positions. For heartbeat extraction, a higher level of agreement between the radar foot end position and the PSG was found. An example of the distinction between thoracic and abdominal breathing using a lateral radar position is shown. Lateral radar positions could lead to a more detailed analysis of movements along the body, with the potential for diagnostic applications.

Transfer Learning for Automatic Sleep Staging Using a Pre-Gelled Electrode Grid.

Novel sensor solutions for sleep monitoring at home could alleviate bottlenecks in sleep medical care as well as enable selective or continuous observation over long periods of time and contribute to new insights in sleep medicine and beyond. Since especially in the latter case the sensor data differ strongly in signal, number and extent of sensors from the classical polysomnography (PSG) sensor technology, an automatic evaluation is essential for the application. However, the training of an automatic algorithm is complicated by the fact that the development phase of the new sensor technology, extensive comparative measurements with standardized reference systems, is often not possible and therefore only small datasets are available. In order to circumvent high system-specific training data requirements, we employ pre-training on large datasets with finetuning on small datasets of new sensor technology to enable automatic sleep phase detection for small test series. By pre-training on publicly available PSG datasets and finetuning on 12 nights recorded with new sensor technology based on a pre-gelled electrode grid to capture electroencephalography (EEG), electrooculography (EOG) and electromyography (EMG), an F1 score across all sleep phases of 0.81 is achieved (wake 0.84, N1 0.62, N2 0.81, N3 0.87, REM 0.88), using only EEG and EOG. The analysis additionally considers the spatial distribution of the channels and an approach to approximate classical electrode positions based on specific linear combinations of the new sensor grid channels.

Pre-gelled Electrode Grid for Self-Applied EEG Sleep Monitoring at Home.

The need for diagnostic capabilities for sleep disorders such as sleep apnea and insomnia far exceeds the capacity of inpatient sleep laboratories. Some home monitoring systems omit electroencephalography (EEG) because trained personnel may be needed to apply EEG sensors. Since EEG is essential for the detailed evaluation of sleep, better systems supporting the convenient and robust recording of sleep EEG at home are desirable. Recent advances in EEG acquisition with flex-printed sensors promise easier application of EEG sensor arrays for chronic recordings, yet these sensor arrays were not designed for sleep EEG. Here we explored the self-applicability of a new sleep EEG sensor array (trEEGrid) without prior training. We developed a prototype with pre-gelled neonatal ECG electrodes placed on a self-adhesive grid shape that guided the fast and correct positioning of a total of nine electrodes on the face and around the ear. Positioning of the sensors was based on the results of a previous ear-EEG sleep study (da Silva Souto et al., 2021), and included electrodes around the ear, one eye, and the chin. For comparison, EEG and electrooculogram channels placed according to the American Academy of Sleep Medicine criteria, as well as respiratory inductance plethysmography on thorax and abdomen, oxygen saturation, pulse and body position were included with a mobile polysomnography (PSG) system. Two studies with 32 individuals were conducted to compare the signal quality of the proposed flex-printed grid with PSG signals and to explore self-application of the new grid at home. Results indicate that the new array is self-applicable by healthy participants without on-site hands-on support. A comparison of the hypnogram annotations obtained from the data of both systems revealed an overall substantial agreement on a group level (Cohen's κ = 0.70 ± 0.01). These results suggest that flex-printed pre-gelled sensor arrays designed for sleep EEG acquisition can facilitate self-recording at home.

Flex-Printed Ear-EEG Sensors for Adequate Sleep Staging at Home.

A comfortable, discrete and robust recording of the sleep EEG signal at home is a desirable goal but has been difficult to achieve. We investigate how well flex-printed electrodes are suitable for sleep monitoring tasks in a smartphone-based home environment. The cEEGrid ear-EEG sensor has already been tested in the laboratory for measuring night sleep. Here, 10 participants slept at home and were equipped with a cEEGrid and a portable amplifier (mBrainTrain, Serbia). In addition, the EEG of Fpz, EOG_L and EOG_R was recorded. All signals were recorded wirelessly with a smartphone. On average, each participant provided data for M = 7.48 h. An expert sleep scorer created hypnograms and annotated grapho-elements according to AASM based on the EEG of Fpz, EOG_L and EOG_R twice, which served as the baseline agreement for further comparisons. The expert scorer also created hypnograms using bipolar channels based on combinations of cEEGrid channels only, and bipolar cEEGrid channels complemented by EOG channels. A comparison of the hypnograms based on frontal electrodes with the ones based on cEEGrid electrodes (κ = 0.67) and the ones based on cEEGrid complemented by EOG channels (κ = 0.75) both showed a substantial agreement, with the combination including EOG channels showing a significantly better outcome than the one without (p = 0.006). Moreover, signal excerpts of the conventional channels containing grapho-elements were correlated with those of the cEEGrid in order to determine the cEEGrid channel combination that optimally represents the annotated grapho-elements. The results show that the grapho-elements were well-represented by the front-facing electrode combinations. The correlation analysis of the grapho-elements resulted in an average correlation coefficient of 0.65 for the most suitable electrode configuration of the cEEGrid. The results confirm that sleep stages can be identified with electrodes placement around the ear. This opens up opportunities for miniaturized ear-EEG systems that may be self-applied by users.

An Evaluation of Motion Trackers with Virtual Reality Sensor Technology in Comparison to a Marker-Based Motion Capture System Based on Joint Angles for Ergonomic Risk Assessment.

The reproduction and simulation of workplaces, and the analysis of body postures during work processes, are parts of ergonomic risk assessments. A commercial virtual reality (VR) system offers the possibility to model complex work scenarios as virtual mock-ups and to evaluate their ergonomic designs by analyzing motion behavior while performing work processes. In this study a VR tracking sensor system (HTC Vive tracker) combined with an inverse kinematic model (Final IK) was compared with a marker-based optical motion capture system (Qualisys). Marker-based optical motion capture systems are considered the gold standard for motion analysis. Therefore, Qualisys was used as the ground truth in this study. The research question to be answered was how accurately the HTC Vive System combined with Final IK can measure joint angles used for ergonomic evaluation. Twenty-six subjects were observed simultaneously with both tracking systems while performing 20 defined movements. Sixteen joint angles were analyzed. Joint angle deviations between ±6∘ and ±42∘ were identified. These high deviations must be considered in ergonomic risk assessments when using a VR system. The results show that commercial low-budget tracking systems have the potential to map joint angles. Nevertheless, substantial weaknesses and inaccuracies in some body regions must be taken into account. Recommendations are provided to improve tracking accuracy and avoid systematic errors.