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Vision training

Runyu L Greene, Yu Hen Hu, Nicholas Difranco, Xuan Wang, Ming-Lun Lu, Stephen Bao, Jia-Hua Lin, Robert G Radwin
OBJECTIVE: A method for automatically classifying lifting postures from simple features in video recordings was developed and tested. We explored if an "elastic" rectangular bounding box, drawn tightly around the subject, can be used for classifying standing, stooping, and squatting at the lift origin and destination. BACKGROUND: Current marker-less video tracking methods depend on a priori skeletal human models, which are prone to error from poor illumination, obstructions, and difficulty placing cameras in the field...
August 9, 2018: Human Factors
Simon J Bennett, Spencer J Hayes, Makoto Uji
Motivated by recent findings of improved perceptual processing and perceptual-motor skill following stroboscopic vision training, the current study examined the performance and acquisition effects of stroboscopic vision methods that afford a different visual experience. In Experiment 1, we conducted a within-subject design study to examine performance of a multiple object tracking (MOT) task in different stroboscopic vision conditions (Nike Vapor Strobe® , PLATO visual occlusion, and intermittent display presentation) operating at 5...
2018: Frontiers in Psychology
W Deiters, A Burmann, S Meister
Digitalization cannot be understood as an off-the-shelf product, bought as a one-time purchase in a warehouse. It rather requires a constantly developing vision, which comes with a continuous transformation process, hand in hand with strategic innovation management. Thus, digitalization means understanding the digital maturity level of an enterprise and the digital skills of the employees. Besides an investment in products, a successful digitalization process also necessitates consideration of the cost to release employees from their obligations in order to contribute to the process as well as for a dedicated and continuing staff training and education program...
August 7, 2018: Der Urologe. Ausg. A
Hailong Li, Nehal A Parikh, Lili He
Early diagnosis remains a significant challenge for many neurological disorders, especially for rare disorders where studying large cohorts is not possible. A novel solution that investigators have undertaken is combining advanced machine learning algorithms with resting-state functional Magnetic Resonance Imaging to unveil hidden pathological brain connectome patterns to uncover diagnostic and prognostic biomarkers. Recently, state-of-the-art deep learning techniques are outperforming traditional machine learning methods and are hailed as a milestone for artificial intelligence...
2018: Frontiers in Neuroscience
Ahmet Sureyya Rifaioglu, Heval Atas, Maria Jesus Martin, Rengul Cetin-Atalay, Volkan Atalay, Tunca Dogan
The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as 'virtual screening' (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets...
July 31, 2018: Briefings in Bioinformatics
Liana E Chase, Kedar Marahatta, Kripa Sidgel, Sujan Shrestha, Kamal Gautam, Nagendra P Luitel, Bhogendra Raj Dotel, Reuben Samuel
Background: The World Health Organization's 'building back better' approach advocates capitalizing on the resources and political will elicited by disasters to strengthen national mental health systems. This study explores the contributions of the response to the 2015 earthquake in Nepal to sustainable mental health system reform. Methods: We systematically reviewed grey literature on the mental health and psychosocial response to the earthquake obtained through online information-sharing platforms and response coordinators (168 documents) to extract data on response stakeholders and activities...
2018: International Journal of Mental Health Systems
Stephen M Modell, Toby Citrin, Sharon L R Kardia
The United States Precision Medicine Initiative (PMI) was announced by then President Barack Obama in January 2015. It is a national effort designed to take into account genetic, environmental, and lifestyle differences in the development of individually tailored forms of treatment and prevention. This goal was implemented in March 2015 with the formation of an advisory committee working group to provide a framework for the proposed national research cohort of one million or more participants. The working group further held a public workshop on participant engagement and health equity, focusing on the design of an inclusive cohort, building public trust, and identifying active participant engagement features for the national cohort...
August 3, 2018: Healthcare (Basel, Switzerland)
Junjie Hu, Yuanyuan Chen, Jie Zhong, Rong Ju, Zhang Yi
Retinopathy of Prematurity (ROP) is a retinal vasproliferative disorder disease principally observed in infants born prematurely with low birth weight. ROP is an important cause of childhood blindness. Although automatic or semiautomatic diagnosis of ROP has been conducted, most previous studies have focused on "plus" disease, which is indicated by abnormalities of retinal vasculature. Few studies have reported methods for identifying the "stage" of ROP disease. Deep neural networks have achieved impressive results in many computer vision and medical image analysis problems, raising expectations that it might be a promising tool in automatic diagnosis of ROP...
August 6, 2018: IEEE Transactions on Medical Imaging
Haifeng Wu, Qing Huang, Daqing Wang, Lifu Gao
The commonly used classifiers for pattern recognition of human motion, like backpropagation neural network (BPNN) and support vector machine (SVM), usually implement the classification by extracting some hand-crafted features from the human biological signals. These features generally require the domain knowledge for researchers to be designed and take a long time to be tested and selected for high classification performance. In contrast, convolutional neural network (CNN), which has been widely applied to computer vision, can learn to automatically extract features from the training data by means of convolution and subsampling, but CNN training usually requires large sample data and has the overfitting problem...
July 26, 2018: Journal of Electromyography and Kinesiology
Chantal Milleret, Emmanuel Bui Quoc
Infantile strabismus impairs the perception of all attributes of the visual scene. High spatial frequency components are no longer visible, leading to amblyopia. Binocularity is altered, leading to the loss of stereopsis. Spatial perception is impaired as well as detection of vertical orientation, the fastest movements, directions of movement, the highest contrasts and colors. Infantile strabismus also affects other vision-dependent processes such as control of postural stability. But presently, rehabilitative therapies for infantile strabismus by ophthalmologists, orthoptists and optometrists are restricted to preventing or curing amblyopia of the deviated eye, aligning the eyes and, whenever possible, preserving or restoring binocular vision during the critical period of development, i...
2018: Frontiers in Systems Neuroscience
Konda Reddy Mopuri, Aditya Ganeshan, Venkatesh Babu Radhakrishnan
Machine learning models are susceptible to adversarial perturbations: small changes to input that can cause large changes in output. Additionally, there exist input-agnostic perturbations, called universal adversarial perturbations, which can change the inference of target model on most of the data samples. However, existing methods to craft universal perturbations are (i) task specific, (ii) require samples from the training data distribution, and (iii) perform complex optimizations. Additionally, fooling ability of the crafted perturbations is proportional to the available training data...
July 31, 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
[Objective: To present the activities that facilitate the development of a public policy by public health and higher and university education ministry stakeholders – based on a common vision of nurses and midwives training in Democratic Republic of the Congo (DRC).Methods: An operational research using different methods applied by experts called “policy brokers” according to a framework covering the advocacy mechanisms (Advocacy Coalition Framework) designed to promote the development of a public policy. The population comprised 2 types of common interest groups (coalitions), derived from 3 systems (sociocultural-legal, educational, professional), involved in the choice of the “secondary AND higher” or “secondary OR higher“ training profile for the concerned professionals. The methods comprised: workshops (discussion, training, restitution, validation, negotiation, scientific, reflection group meetings), training activities (programme development, training of nursing and midwives trainers-supervisors) and a variety of media coverage and marketing activities.Results: The nurses and midwives profiles required in the DRC have been established. The levels required for their training have been validated and defined by a common vision of the two ministries concerned. A formal consultation framework was set up to launch the required reform for the review of these two professional’s profiles.Conclusion: The public policy experts’ activities based on the advocacy framework are complex, lengthy and time-consuming. In DRC, a Ministerial decree is currently being finalized to address the creation of a formal consultation framework concerning the training and utilisation of human health resources].
Marie Hatem, Hana Halabi-Nassif, Marie Maroun
OBJECTIVE: To present the activities that facilitate the development of a public policy by public health and higher and university education ministry stakeholders - based on a common vision of nurses and midwives training in Democratic Republic of the Congo (DRC). METHODS: An operational research using different methods applied by experts called ?policy brokers? according to a framework covering the advocacy mechanisms (Advocacy Coalition Framework) designed to promote the development of a public policy...
March 3, 2018: Santé Publique: Revue Multidisciplinaire Pour la Recherche et L'action
Angelica C Scanzera, Ellen Shorter
SIGNIFICANCE: Familial dysautonomia is a rare genetic disorder that affects the sensory and autonomic nervous systems. Affected individuals have decreased corneal sensation and can develop serious complications from neurotrophic keratitis. Scleral devices are an excellent option for the long-term management of patients with familial dysautonomia and neurotrophic keratitis. PURPOSE: In this series, we describe three patients with familial dysautonomia and classic ocular complications fit with scleral devices...
July 30, 2018: Optometry and Vision Science: Official Publication of the American Academy of Optometry
Sophie Staniszewska, Simon Denegri, Rachel Matthews, Virginia Minogue
OBJECTIVES: To review the progress of public involvement (PPI) in NIHR (National Institute for Health Research) research, identify barriers and enablers, reflect on the influence of PPI on the wider health research system in the UK and internationally and develop a vision for public involvement in research for 2025. The developing evidence base, growing institutional commitment and public involvement activity highlight its growth as a significant international social movement. DESIGN: The 'Breaking Boundaries Review' was commissioned by the Department of Health...
July 30, 2018: BMJ Open
Bin Xue, Ningning Tong
Inverse synthetic aperture radar (ISAR) object detection is one of the most important and challenging problems in computer vision tasks. To provide a convenient and high-quality ISAR object detection method, a fast and efficient weakly semi-supervised method, called deep ISAR object detection (DIOD), is proposed, based on advanced region proposal networks (ARPNs) and weakly semi-supervised deep joint sparse learning: 1) to generate high-level region proposals and localize potential ISAR objects robustly and accurately in minimal time, ARPN is proposed based on a multiscale fully convolutional region proposal network and a region proposal classification and ranking strategy...
July 27, 2018: IEEE Transactions on Cybernetics
Chengquan Zhou, Dong Liang, Xiaodong Yang, Hao Yang, Jibo Yue, Guijun Yang
The number of wheat ears in the field is very important data for predicting crop growth and estimating crop yield and as such is receiving ever-increasing research attention. To obtain such data, we propose a novel algorithm that uses computer vision to accurately recognize wheat ears in a digital image. First, red-green-blue images acquired by a manned ground vehicle are selected based on light intensity to ensure that this method is robust with respect to light intensity. Next, the selected images are cut to ensure that the target can be identified in the remaining parts...
2018: Frontiers in Plant Science
Zhuorong Li, Wanliang Wang, Yanwei Zhao
Image translation, where the input image is mapped to its synthetic counterpart, is attractive in terms of wide applications in fields of computer graphics and computer vision. Despite significant progress on this problem, largely due to a surge of interest in conditional generative adversarial networks (cGANs), most of the cGAN-based approaches require supervised data, which are rarely available and expensive to provide. Instead we elaborate a common framework that is also applicable to the unsupervised cases, learning the image prior by conditioning the discriminator on unaligned targets to reduce the mapping space and improve the generation quality...
2018: Computational Intelligence and Neuroscience
Carolyn Ton, Abdelmalak Omar, Vitaliy Szedenko, Viet Hung Tran, Alina Aftab, Fabiana Perla, Michael J Bernstein, Yi Yang
Echolocation enables people with impaired or no vision to comprehend the surrounding spatial information through reflected sound. However, this technique often requires substantial training and the accuracy of echolocation is subject to various conditions. Furthermore, the individuals who practice this sensing method must simultaneously generate the sound and process the received audio information. This paper proposes and evaluates a proof of concept Light Detection and Ranging (LIDAR) Assist Spatial Sensing (LASS) system, which intends to overcome these restrictions by obtaining the spatial information of the user's surroundings through a LIDAR sensor and translating the spatial information into stereo sound of various pitches...
July 25, 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Ren-Jie Huang, Chun-Yu Tsao, Yi-Pin Kuo, Yi-Chung Lai, Chi Chung Liu, Zhe-Wei Tu, Jung-Hua Wang, Chung-Cheng Chang
Recently, an upsurge of deep learning has provided a new direction for the field of computer vision and visual tracking. However, expensive offline training time and the large number of images required by deep learning have greatly hindered progress. This paper aims to further improve the computational performance of CNT which is reported to deliver 5 fps performance in visual tracking, we propose a method called Fast-CNT which differs from CNT in three aspects: firstly, an adaptive k value (rather than a constant 100) is determined for an input video; secondly, background filters used in CNT are omitted in this work to save computation time without affecting performance; thirdly, SURF feature points are used in conjunction with the particle filter to address the drift problem in CNT...
July 24, 2018: Sensors
Maria Crotty, Maayken van den Berg, Allison Hayes, Celia Chen, Kylie Lange, Stacey George
BACKGROUND: Homonymous hemianopia post-stroke reduces independence. OBJECTIVE: To compare the effectiveness of a standardised program versus current individualised therapy in patients with homonymous hemianopia. METHODS: Single-blind randomized controlled trial, 24 patients (54% male), mean age (65±4.3), mean time since stroke (51±52.3 days), recruited from rehabilitation and vision services in Adelaide, Australia. Participants were randomized to a combined standardized scanning and mobility program of 7 weeks, 3 times per week or to individualized therapy recommended by clinicians...
July 17, 2018: NeuroRehabilitation
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