Search

Sleep quality is an essential parameter of a healthy human life, while sleep disorders such as sleep apnea are abundant. In the investigation of sleep and its malfunction, the gold-standard is polysomnography, which utilizes an extensive range of variables for sleep stage classification. However, undergoing full polysomnography, which requires many sensors that are directly connected to the heaviness of the setup and the discomfort of sleep, brings a significant burden. In this study, sleep stage classification was performed using the single dimension of nasal pressure, dramatically decreasing the complexity of the process. In turn, such improvements could increase the much needed clinical applicability. Specifically, we propose a deep learning structure consisting of multi-kernel convolutional neural networks and bidirectional long short-term memory for sleep stage classification. Sleep stages of 25 healthy subjects were classified into 3-class (wake, rapid eye movement (REM), and non-REM) and 4-class (wake, REM, light, and deep sleep) based on nasal pressure. Following a leave-one-subject-out cross-validation, in the 3-class the accuracy was 0.704, the F1-score was 0.490, and the kappa value was 0.283 for the overall metrics. In the 4-class, the accuracy was 0.604, the F1-score was 0.349, and the kappa value was 0.217 for the overall metrics. This was higher than the four comparative models, including the class-wise F1-score. This result demonstrates the possibility of a sleep stage classification model only using easily applicable and highly practical nasal pressure recordings. This is also likely to be used with interventions that could help treat sleep-related diseases.

Accurate pose estimation of planar objects is a key computation in visual localization tasks, with recent studies showing remarkable progress on a handful of baseline datasets. Nonetheless, achieving similar performance on sequences in unconstrained environments is still an ongoing quest to be accomplished, largely due to the existence of several sources of errors, which are correlated but often only partly tackled in the literature. In this article, we propose POP, a generic real-time planar-object pose-estimation framework which is designed to handle the aforementioned types of errors while not losing generality to a specific choice of keypoint detection or tracking algorithm. The essence of POP lies in activating keypoint detection module in the background as well as adding several refinement steps in order to reduce correlated sources of errors within the pipeline. We provide extensive experimental evaluations against state-of-the-art planar object tracking algorithms on baseline and more challenging datasets, empirically demonstrating the effectiveness of the POP framework for scenes with large environmental variations.

Background/Aims Chronic liquid and/or food stasis caused by retention esophagitis (RE) in achalasia is a notable endoscopic finding because of the presence of a thickened or whitish esophageal mucosa and histologically altered squamous hyperplasia. We aimed to identify the clinical features of RE associated with achalasia and to clarify the clinical definition of RE in achalasia as a precancerous lesion identified by analyzing biomarker expressions. Methods From 2006 to 2015, we retrospectively reviewed 37 patients with achalasia without previous treatment. Among them, 21 patients had diagnostic findings of RE (RE+) and 16 patients had no diagnostic findings of RE (RE–). Immunohistochemical staining of p53, p16, and Ki-67 was performed on the endoscopic biopsy tissues from the patients with achalasia and 10 control patients with non-obstructive dysphagia. Results The symptom duration and transit delay were significantly longer in the RE+ group than in the RE– group. We found particularly high p53 positivity rates in the RE+ group (p

Facial expressions are one of the important non-verbal ways used to understand human emotions during communication. Thus, acquiring and reproducing facial expressions is helpful in analyzing human emotional states. However, owing to complex and subtle facial muscle movements, facial expression modeling from images with face poses is difficult to achieve. To handle this issue, we present a method for acquiring facial expressions from a non-frontal single photograph using a 3D-aided approach. In addition, we propose a contour-fitting method that improves the modeling accuracy by automatically rearranging 3D contour landmarks corresponding to fixed 2D image landmarks. The acquired facial expression input can be parametrically manipulated to create various facial expressions through a blendshape or expression transfer based on the FACS (Facial Action Coding System). To achieve a realistic facial expression synthesis, we propose an exemplar-texture wrinkle synthesis method that extracts and synthesizes appropriate expression wrinkles according to the target expression. To do so, we constructed a wrinkle table of various facial expressions from 400 people. As one of the applications, we proved that the expression-pose synthesis method is suitable for expression-invariant face recognition through a quantitative evaluation, and showed the effectiveness based on a qualitative evaluation. We expect our system to be a benefit to various fields such as face recognition, HCI, and data augmentation for deep learning.

Purpose: The purpose of this study was to explore the relationships among preschoolers’ smartphone addiction tendency, problem behaviors, and parenting efficacy of the mothers of these children. Methods: A cross-sectional, descriptive study design was used with self-administered questionnaires. A total of 83 mothers of preschoolers aged 4-6 years and their preschool teachers from a kindergarten participated in the study. Results: Mean daily time spent by the preschoolers on smartphones was about 45 minutes/day. About 40% spent more than 60 minutes/day on smartphones. The majority (69.8%) used smartphones without adult supervision. The level of smartphone addiction tendency among the pre-schoolers was 1.52±0.45 on a 4-point scale. Preschoolers whose mothers use smartphones for more than 60 minutes/day showed a higher level of smartphone addiction tendency than preschoolers whose mothers use smartphones for less than 60 minutes/day. Also smartphone addiction tendency among preschoolers had a positive correlation with hyperactive-distractible behavior (r = .228, p = .038) and a negative correlation with parenting efficacy of their mothers (r = -.299, p = .006). Conclusion: Our findings demonstrated that preschooler’s smartphone addiction tendency is significantly associated with hyperactive-distractible behaviors of the children and low parenting efficacy among their mothers. Therefore well-designed care plans for these populations should be provided to decrease smartphone addiction tendency.

Face recognition is one research area that has benefited from the recent popularity of deep learning, namely the convolutional neural network (CNN) model. Nevertheless, the recognition performance is still compromised by the model’s dependency on the scale of input images and the limited number of feature maps in each layer of the network. To circumvent these issues, we propose PSI-CNN, a generic pyramid-based scale-invariant CNN architecture which additionally extracts untrained feature maps across multiple image resolutions, thereby allowing the network to learn scale-independent information and improving the recognition performance on low resolution images. Experimental results on the LFW dataset and our own CCTV database show PSI-CNN consistently outperforming the widely-adopted VGG face model in terms of face matching accuracy.

In this paper, we present a novel framework for automatically assessing facial attractiveness that considers four ratio feature sets as objective elements of facial attractiveness. In our framework, these feature sets are combined with three regression-based predictors to estimate a facial beauty score. To enhance the system’s performance to make it comparable with human scoring, we apply a score fusion technique. Experimental results show that the attractiveness score obtained by the proposed framework better correlates with human assessments than the scores from other predictors. The framework’s modularity allows any features or predictors to be integrated into the facial attractiveness measure. Our proposed framework can be applied to many beauty-related fields, such as the plastic surgery, cosmetics, and entertainment industries.