Enabling Psychiatrists to Explore the Full Potential of E-Health

10.3389/fpsyt.2015.00177Frontiers in Psychiatry6DEC17

A Smartphone App for Attentional Bias Retraining in Smokers: Mixed Methods Pilot Study

Background: Smoking is a global health threat. Attentional bias influences smoking behaviors. Although attentional bias retraining has shown benefits and recent advances in technology suggest that attentional bias retraining can be delivered via smartphone apps, there is a paucity of research on this topic. Objective: This study aims to address this gap by exploring the use of attentional bias retraining via a novel smartphone app using a mixed methods pilot study. In the quantitative phase, it is hypothesized that participants in the training group who undertake attentional bias retraining via the app should have decreased levels of attentional bias, subjective craving, and smoking frequency, compared with those in the control group who do not undertake attentional bias retraining. The qualitative phase explores how the participants perceive and experience the novel app. Methods: In all, 10 adult smokers (3 females and 7 males) between the ages of 26 and 56 years (mean 34.4 years, SD 9.97 years) were recruited. The participants were randomly allocated to the training and control groups. In weeks 1 and 3, participants from both groups attempted the standard visual probe task and rated their smoking frequency and subjective craving. In week 2, the participants in the training group attempted the modified visual probe task. After week 3, participants from both groups were interviewed about their views and experiences of the novel app. Results: The results of the quantitative analysis did not support this study’s hypothesis. The qualitative data were analyzed using thematic analysis. The results yielded 5 themes: ease, helpfulness, unhelpful aspects, barriers, and refinement. Conclusions: Findings from the qualitative study were consistent with those from previous studies on health-related smartphone apps. The qualitative results were helpful in understanding the user perspectives and experiences of the novel app, indicating that future research in this innovative area is necessary

Gender recognition from facial images: Two or three dimensions?

© 2016 Optical Society of America. This paper seeks to compare encoded features from both two-dimensional (2D) and three-dimensional (3D) face images in order to achieve automatic gender recognition with high accuracy and robustness. The Fisher vector encoding method is employed to produce 2D, 3D, and fused features with escalated discriminative power. For 3D face analysis, a two-source photometric stereo (PS) method is introduced that enables 3D surface reconstructions with accurate details as well as desirable efficiency. Moreover, a 2D + 3D imaging device, taking the two-source PS method as its core, has been developed that can simultaneously gather color images for 2D evaluations and PS images for 3D analysis. This system inherits the superior reconstruction accuracy from the standard (three or more light) PS method but simplifies the reconstruction algorithm as well as the hardware design by only requiring two light sources. It also offers great potential for facilitating human computer interaction by being accurate, cheap, efficient, and nonintrusive. Ten types of low-level 2D and 3D features have been experimented with and encoded for Fisher vector gender recognition. Evaluations of the Fisher vector encoding method have been performed on the FERET database, Color FERET database, LFW database, and FRGCv2 database, yielding 97.7%, 98.0%, 92.5%, and 96.7% accuracy, respectively. In addition, the comparison of 2D and 3D features has been drawn from a self-collected dataset, which is constructed with the aid of the 2D + 3D imaging device in a series of data capture experiments. With a variety of experiments and evaluations, it can be proved that the Fisher vector encoding method outperforms most state-of-the-art gender recognition methods. It has also been observed that 3D features reconstructed by the two-source PS method are able to further boost the Fisher vector gender recognition performance, i.e., up to a 6% increase on the self-collected database

Photometric stereo for three-dimensional leaf venation extraction

© 2018 Elsevier B.V. Leaf venation extraction studies have been strongly discouraged by considerable challenges posed by venation architectures that are complex, diverse and subtle. Additionally, unpredictable local leaf curvatures, undesirable ambient illuminations, and abnormal conditions of leaves may coexist with other complications. While leaf venation extraction has high potential for assisting with plant phenotyping, speciation and modelling, its investigations to date have been confined to colour image acquisition and processing which are commonly confounded by the aforementioned biotic and abiotic variations. To bridge the gaps in this area, we have designed a 3D imaging system for leaf venation extraction, which can overcome dark or bright ambient illumination and can allow for 3D data reconstruction in high resolution. We further propose a novel leaf venation extraction algorithm that can obtain illumination-independent surface normal features by performing Photometric Stereo reconstruction as well as local shape measures by fusing the decoupled shape index and curvedness features. In addition, this algorithm can determine venation polarity – whether veins are raised above or recessed into a leaf. Tests on both sides of different leaf species with varied venation architectures show that the proposed method is accurate in extracting the primary, secondary and even tertiary veins. It also proves to be robust against leaf diseases which can cause dramatic changes in colour. The effectiveness of this algorithm in determining venation polarity is verified by it correctly recognising raised or recessed veins in nine different experiments

Smartphone application in postgraduate clinical psychology training: trainees’ perspectives

M-learning refers to the learning that takes advantage of mobile technologies. Although research shows enhanced educational outcomes from m-learning in some Asian countries, the generalizability to postgraduate clinical psychology training in Singapore remains unclear. Current professional standards in clinical psychology training emphasize the importance of attainment of clinical competencies in trainees. Although learning theories indicated potential for m-learning to be incorporated into the local clinical psychology curriculum, trainees’ perspectives have not been adequately explored on m-learning. The study aimed to address this gap by exploring the use of m-learning via a novel smartphone application in clinical psychology training using mixed-methods design. Eight clinical psychology trainees between the ages of 26 to 43 years old (mean age of 31.75, SD = 5.49) enrolled in a relevant coursework subject were recruited. Participants were randomly allocated to the experimental and control groups. The experimental group accessed the novel application weekly, from week 1 to week 6, and participants in the control group accessed the application after week 6. Participants from both groups completed a brief demographic questionnaire, and the following scales New General Self-Efficacy Scale adapted for Education (NGSES-E) and self-reported scale of learning outcomes (SLO). The qualitative study explored how participants perceived and experienced the novel application. Participants from the experimental group were invited to provide open-ended responses about the novel application. Data were analyzed using thematic analysis. Results from the qualitative analysis yielded four themes of: Convenience, preferred learning style, building confidence, and putting theory into practice. Findings from the qualitative study were consistent with previous studies about advantages of m-learning: That the e-platform was convenient, the learning style was engaging, which helped to build confidence, and facilitate practical learning of skills. The qualitative results were helpful in understanding the users’ perspectives and experience of the novel application, indicating that future research in this innovative area is necessary. However, the quantitative outcomes were not significant, limitations would be discussed, and recommendations made for future research

Innovative 3D and 2D machine vision methods for analysis of plants and crops in the field

© 2018 Elsevier B.V. Machine vision systems offer great potential for automating crop control, harvesting, fruit picking, and a range of other agricultural tasks. However, most of the reported research on machine vision in agriculture involves a 2D approach, where the utility of the resulting data is often limited by effects such as parallax, perspective, occlusion and changes in background light – particularly when operating in the field. The 3D approach to plant and crop analysis described in this paper offers potential to obviate many of these difficulties by utilising the richer information that 3D data can generate. The methodologies presented, such as four-light photometric stereo, also provide advanced functionalities, such as an ability to robustly recover 3D surface texture from plants at very high resolution. This offers potential for enabling, for example, reliable detection of the meristem (the part of the plant where growth can take place), to within a few mm, for directed weeding (with all the associated cost and ecological benefits) as well as offering new capabilities for plant phenotyping. The considerable challenges associated with robust and reliable utilisation of machine vision in the field are also considered and practical solutions are described. Two projects are used to illustrate the proposed approaches: a four-light photometric stereo apparatus able to recover plant textures at high-resolution (even in direct sunlight), and a 3D system able to measure potato sizes in-the-field to an accuracy of within 10%, for extended periods and in a range of environmental conditions. The potential benefits of the proposed 3D methods are discussed, both in terms of the advanced capabilities attainable and the widespread potential uptake facilitated by their low cost

Eye centre localisation: An unsupervised modular approach

© Emerald Group Publishing Limited. Purpose - This paper aims to introduce an unsupervised modular approach for eye centre localisation in images and videos following a coarse-to-fine, global-to-regional scheme. The design of the algorithm aims at excellent accuracy, robustness and real-time performance for use in real-world applications. Design/methodology/approach - A modular approach has been designed that makes use of isophote and gradient features to estimate eye centre locations. This approach embraces two main modalities that progressively reduce global facial features to local levels for more precise inspections. A novel selective oriented gradient (SOG) filter has been specifically designed to remove strong gradients from eyebrows, eye corners and self-shadows, which sabotage most eye centre localisation methods. The proposed algorithm, tested on the BioID database, has shown superior accuracy. Findings - The eye centre localisation algorithm has been compared with 11 other methods on the BioID database and six other methods on the GI4E database. The proposed algorithm has outperformed all the other algorithms in comparison in terms of localisation accuracy while exhibiting excellent real-time performance. This method is also inherently robust against head poses, partial eye occlusions and shadows. Originality/value - The eye centre localisation method uses two mutually complementary modalities as a novel, fast, accurate and robust approach. In addition, other than assisting eye centre localisation, the SOG filter is able to resolve general tasks regarding the detection of curved shapes. From an applied point of view, the proposed method has great potentials in benefiting a wide range of real-world human-computer interaction (HCI) applications

A detailed study of the accretion disk surrounding the high-mass protostar NGC 7538S

We present deep high angular resolution observations of the high-mass protostar NGC 7538S, which is in the center of a cold dense cloud core with a radius of 0.5 pc and a mass of ~2,000 Msun. These observations show that NGC 7538S is embedded in a compact elliptical core with a mass of 85 - 115 Msun. The star is surrounded by a rotating accretion disk, which powers a very young, hot molecular outflow approximately perpendicular to the rotating accretion disk. The accretion rate is very high, ~ 1.4 - 2.8 10^-3 Msun yr^-1. Evidence for rotation of the disk surrounding the star is seen in all largely optically thin molecular tracers, H13CN J = 1-0, HN13C J = 1-0, H13CO+ J = 1-0, and DCN J = 3-2. Many molecules appear to be affected by the hot molecular outflow, including DCN and H13CO+. The emission from CH3CN, which has often been used to trace disk rotation in young high-mass stars, is dominated by the outflow, especially at higher K-levels. Our new high-angular resolution observations show that the rotationally supported part of the disk is smaller than we previously estimated. The enclosed mass of the inner, rotationally supported part of the disk (D ~ 5", i.e 14,000 AU) is ~ 14 - 24 Msun.Comment: Accepted by ApJ; 20 pages, 20 figure

Eye center localization and gaze gesture recognition for human-computer interaction

© 2016 Optical Society of America. This paper introduces an unsupervised modular approach for accurate and real-time eye center localization in images and videos, thus allowing a coarse-to-fine, global-to-regional scheme. The trajectories of eye centers in consecutive frames, i.e., gaze gestures, are further analyzed, recognized, and employed to boost the human-computer interaction (HCI) experience. This modular approach makes use of isophote and gradient features to estimate the eye center locations. A selective oriented gradient filter has been specifically designed to remove strong gradients from eyebrows, eye corners, and shadows, which sabotage most eye center localization methods. A real-world implementation utilizing these algorithms has been designed in the form of an interactive advertising billboard to demonstrate the effectiveness of our method for HCI. The eye center localization algorithm has been compared with 10 other algorithms on the BioID database and six other algorithms on the GI4E database. It outperforms all the other algorithms in comparison in terms of localization accuracy. Further tests on the extended Yale Face Database b and self-collected data have proved this algorithm to be robust against moderate head poses and poor illumination conditions. The interactive advertising billboard has manifested outstanding usability and effectiveness in our tests and shows great potential for benefiting a wide range of real-world HCI applications

Adaptive selection of members for convective-permitting regional ensemble prediction over the western Maritime Continent

A common issue faced by the downscaled regional ensemble prediction systems is the under-dispersiveness of the ensemble forecasts, often attributed to the lack of spread under the initial conditions from the global ensemble. In this study, a novel method that adopts an adaptive approach to selecting global ensemble members for regional downscaling has been developed. Instead of using a fixed set of pre-selected global ensemble members, the adaptive selection performs a sampling algorithm and selects the global ensemble members, which maximizes a fractions skill score (FSS)-based displacement between ensemble members. The method is applied to a convective-permitting ensemble prediction system over the western Maritime Continent, referred to as SINGV-EPS. SINGV-EPS has a grid spacing of 4.5 km and is a 12-member ensemble that is driven by the European Centre for Medium-Range Weather Forecasts (ECMWF) 51-member global ensemble. Month-long trials were conducted in June 2020 to assess the impact of adaptive selection on the ensemble forecast spread and rainfall verification scores. In both fixed pre-selection and adaptive selection experiments, SINGV-EPS was still under-dispersive. However, adaptive selection improved the ensemble spread and reduced the root-mean-square error (RMSE) of the ensemble mean in wind, temperature, and precipitation fields. Further verification of the rainfall forecasts showed that there was a reduction in the Brier score and a higher hit rate in the relative operating characteristic (ROC) curve for all rainfall thresholds when adaptive selection was applied. Additionally, the ensemble mean forecasts from adaptive selection experiments are more accurate beyond 24 h, with a higher FSS for all rainfall thresholds and neighborhood lengths. These results suggest that the adaptive selection is superior to the fixed pre-selection of global ensemble members for downscaled regional ensemble prediction