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We aim to build a system incorporating electroencephalography (EEG) and augmented reality (AR) that is capable of identifying the presence of visual spatial neglect (SN) and mapping the estimated neglected visual field. An EEG-based brain-computer interface (BCI) was used to identify those spatiospectral features that best detect participants with SN among stroke survivors using their EEG responses to ipsilesional and contralesional visual stimuli. Frontal-central delta and alpha, frontal-parietal theta, Fp1 beta, and left frontal gamma were found to be important features for neglect detection. Additionally, temporal analysis of the responses shows that the proposed model is accurate in detecting potentially neglected targets. These targets were predicted using common spatial patterns as the feature extraction algorithm and regularized discriminant analysis combined with kernel density estimation for classification. With our preliminary results, our system shows promise for reliably detecting the presence of SN and predicting visual target responses in stroke patients with SN. [ABSTRACT FROM AUTHOR]

Every person spends around one third of his/her life in bed. For an infant or a young toddler, this percentage can be much higher, and for bed-bound patients it can go up to 100% of their time. In-bed pose estimation is a critical step in many human behavior monitoring systems that are focused on prevention, prediction, and management of at-rest or sleep-related conditions in both adults and children. The topic of automatic noncontact human pose estimation has received a lot of attention/success, especially in the last few years in the computer vision community, thanks to the introduction of deep learning and its power in artificial intelligence (AI) modeling. However, the state-of-the-art (SOTA) vision-based AI algorithms in this field can hardly work under the challenges associated with in-bed human behavior monitoring, such as significant illumination changes (e.g., full darkness at night), heavy occlusion (e.g., covering by a sheet or blanket), as well as the privacy concerns that mitigate large-scale data collection, necessary for any deep learning-based model training. The data quality challenges and privacy concerns have hindered the use of advanced vision-based in-bed behavior monitoring systems at home, which during the recent COVID-19 pandemic could have been an effective way to control the spread of the virus by avoiding in-person visits to clinics. [ABSTRACT FROM AUTHOR]

Compared with the conventional eddy current, pulsed eddy current (PEC) technology has the characteristic of abundant components in the frequency domain, which is a significant technological advancement. Improvements in the production and performance of PEC sensors are necessary for nondestructive testing. Sensor configuration parameters have significant influence on the development of PEC system. In this study, through finite element simulation and experimental investigation, different optimization methods for PEC sensors are compared and studied. According to the eddy current detection principle, a two-dimensional finite element simulation model was established to study the influence of the shape and size parameters of cone-shaped PEC sensors. Then a cone-shaped PEC sensor was optimized by the orthogonal methodology and the response surface methodology, respectively. To compare the effectiveness of the optimization methods, two optimized sensors were fabricated. It was demonstrated that the combination of finite element method and response surface methodology was superior for improving the detection performance of the PEC sensor compared with the orthogonal methodology. The result also indicated that the developed cone-shaped PEC sensor had a stronger defect detection capability than cylindrical-shaped PEC sensors. The proposed optimization design method provides a feasible reference method for the design of PEC sensors and corresponding parameters. [ABSTRACT FROM AUTHOR]