Video-based learning

Parkinson's disease (PD) is a degenerative, progressive disorder of the central nervous system that mainly affects motor control. The aim of this study was to develop data-driven methods and test their clinimetric properties to detect and quantify PD motor states using motion sensor data from leg agility tests. Nineteen PD patients were recruited in a levodopa single dose challenge study. PD patients performed leg agility tasks while wearing motion sensors on their lower extremities. Clinical evaluation of video recordings was performed by three movement disorder specialists who used four items from the motor section of the Unified PD Rating Scale (UPDRS), the treatment response scale (TRS) and a dyskinesia score...

OBJECTIVES: Oral case presentations following resident-patient interactions provide the primary mechanism by which faculty supervisors assess resident competence. However, the extent to which these presentations capture the content and quality of resident-patient communication during the encounter remains unknown. We aimed to determine whether: (i) the resident-patient encounter content matched information conveyed in the case presentation; (ii) the quality of resident-patient communication was accurately conveyed, and (iii) supervisors addressed effective and ineffective communication processes...

Existing online learning evaluation methods do not accurately reflect learning effects, which only considers test and assignment scores. A comprehensive evaluation algorithm is proposed in this paper based on the big data of learning behavior. The conversion ratio is taken into account, which is defined by information entropy theory. The algorithm comprehensively considers the learner's multiple learning behaviors, such as viewing videos, doing exercises, taking exams, participating in discussions. The new evaluation algorithm can help learners understand the learning state and maintain their interest...

BACKGROUND: Web-based learning (WBL), instruction facilitated through the Internet, has demonstrated utility in classroom and clinical education settings; however, there is a void of literature about the use of WBL by clinical educators within pharmacy. The purpose of this research is to evaluate a WBL initiative within clinical pharmacy education. METHODS: Based on the results of a pilot survey, 10 asynchronous WBL clinical modules (videos and interactive patient cases) were developed for pharmacy educators and students in clinical education affiliated with two schools of pharmacy in the midwest USA...

Information technology has stimulated efforts to reform teaching methods in dental education. Most of these efforts involve a shift from conventional mode to the more technology-savvy and student-centered approach. The aim of this study was to compare postgraduate dental students' academic performance using two teaching methodologies (video-based learning and blended module-based learning) in a biostatistics and research methodology course in a master's program at a dental college in India. This retrospective study involved two groups of students enrolled in the master's program with different years of admission: Group I in 2013-14 (n=80) and Group II in 2015-16 (n=80)...

AIM: To evaluate the reliability of the Motor Learning Strategies Rating Instrument (MLSRI-20) in gait-based, video-recorded physiotherapy interventions for children with cerebral palsy (CP). METHOD: Thirty videos of 18 children with CP, aged 6 to 17 years, participating in either traditional or Lokomat-based physiotherapy interventions were rated using the MLSRI-20. Physiotherapist raters provided general and item-specific feedback after rating each video, which was used when interpreting reliability results...

OBJECTIVE: Design, implement, and evaluate the effectiveness of a video-based online training addressing prenatal nutrition for paraprofessional peer educators. METHODS: Quasi-experimental pre-posttest study with 2 groups of paraprofessionals working for the Expanded Food and Nutrition Education Program in 17 states and US territories: intervention (n = 67) and delayed intervention comparison group (n = 64). An online training was systematically developed using Smith and Ragan's instructional design model, the Cognitive Theory of Multimedia Learning, principles of adult learning, and selected constructs of the Social Cognitive Theory...

Nowadays, video quality assessment (VQA) is essential to video compression technology applied to video transmission and storage. However, small-scale video quality databases with imbalanced samples and low-level feature representations for distorted videos impede the development of VQA methods. In this paper, we propose a full-reference (FR) VQA metric integrating transfer learning with a convolutional neural network (CNN). First, we imitate the feature-based transfer learning framework to transfer the distorted images as the related domain, which enriches the distorted samples...

BACKGROUND: To meet the patients' needs and to provide adequate health care, students need to be prepared for interprofessional collaborative practice during their undergraduate education. On interprofessional training wards (IPTW) undergraduates of various health care professions potentially develop a mutual understanding and improve their interprofessional competencies in clinical practice. To enhance collaboration of 6th-year medical students and nursing trainees in the third year of their vocational training an IPTW (Heidelberger Interprofessionelle Ausbildungsstation - HIPSTA) was implemented at the University Hospital Heidelberg, Germany...

The visual magnocellular system is thought to play a crucial role in learning to read. Here therefore, we examined whether magnocellular based training could improve reading in children with visual reading problems. The participants were 24 male primary school students aged between 9-11 (Mean = 9.76, SD = 0.59) with specific reading difficulty. Experimental and control groups were matched for age, sex, educational level, IQ, reading abilities (measured by APRA), magnocellular performance as assessed by a random dot kinematogram (RDK) paradigm and recordings of their saccadic eye movements...

We present the global deep video representation learning to video-based person reidentification (re-ID) that aggregates local 3-D features across the entire video extent. Existing methods typically extract frame-wise deep features from 2-D convolutional networks (ConvNets) which are pooled temporally to produce the video-level representations. However, 2-D ConvNets lose temporal priors immediately after the convolutions, and a separate temporal pooling is limited in capturing human motion in short sequences...

A substantial proportion of patients with functional neurological disorders (FND) are being incorrectly diagnosed with epilepsy because their semiology resembles that of epileptic seizures (ES). Misdiagnosis may lead to unnecessary treatment and its associated complications. Diagnostic errors often result from an over-reliance on specific clinical features. Furthermore, the lack of electrophysiological changes in patients with FND can also be seen in some forms of epilepsy, making diagnosis extremely challenging...

Videos are spatio-temporally rich in static to dynamic objects/scenes, sparse to dense, and periodic to non-periodic motions. Particularly, dynamic texture (DT) exhibits complex appearance and motion changes that remain challenge to deal with. This paper presents an energy optimization method for feature extraction and recognition in videos. For noise and background jitter, Tikhonov regularization (TR) with eigen-vector and Frenet-Serret formula based energy constraints is also proposed. Different periodicity of DT can be adapted by the time-varying number of learning temporal frames...

Recently, convolutional neural network (CNN) has attracted tremendous attention and achieved great success in many image processing tasks. In this paper, we focus on CNN technology joining with image restoration to facilitate video coding performance, and propose the content-aware CNN based in-loop filtering for High Efficiency Video Coding (HEVC). In particular, we quantitatively analyze the structure of the proposed CNN model from multiple dimensions to make the model interpretable and optimal for CNN based loop filtering...

Video super-resolution (VSR) has become one of the most critical problems in video processing. In the deep learning literature, recent works have shown the benefits of using adversarial-based and perceptual losses to improve the performance on various image restoration tasks; however, these have yet to be applied for video super-resolution. In this work, we propose a Generative Adversarial Network(GAN)-based formulation for VSR. We introduce a new generator network optimized for the VSR problem, named VSRResNet, along with a new discriminator architecture to properly guide VSRResNet during the GAN training...

We describe a new graphical user interface-based application, US-Pro, designed to enable customized, high-throughput ultrasound video anonymization and dynamic cropping before output to video or high-efficiency disk storage. This application is distributed in a Docker container environment, which supports facile software installation on the most commonly used operating systems, as well as local processing of data sets, precluding the external transfer of electronic protected health information. The US-Pro application will facilitate the reproducible production of large-scale ultrasound video data sets for varied applications, including machine-learning analysis, educational distribution, and quality assurance review...

Accurately determining pain levels in children is difficult, even for trained professionals and parents. Facial activity provides sensitive and specific information about pain, and computer vision algorithms have been developed to automatically detect Facial Action Units (AUs) defined by the Facial Action Coding System (FACS). Our prior work utilized information from computer vision, i.e., automatically detected facial AUs, to develop classifiers to distinguish between pain and no-pain conditions. However, application of pain/no-pain classifiers based on automated AU codings across different environmental domains results in diminished performance...

BACKGROUND: Good clinician-patient communication is essential to provide quality health care and key to patient-centered care. However, individuals and organizations seeking to improve in this area face significant challenges. A major barrier is the absence of an efficient system for assessing clinicians' communication skills and providing meaningful, individual-level feedback. OBJECTIVE: The purpose of this paper is to describe the design and creation of the Video-Based Communication Assessment (VCA), an innovative, flexible system for assessing and ultimately enhancing clinicians' communication skills...

PURPOSE: The purpose of this review is to provide an update on current management and recent research for amblyopia treatment. Part I will review patching, atropine penalization, and pharmacological treatments. Part II will focus on perceptual learning, video gaming, and binocular dichoptic approaches. METHODS: A literature search was performed in PubMed, ClinicalTrials.gov , Google Scholar, and reference lists of retrieved articles until December 20, 2018, for all papers containing "amblyopia treatment" or "amblyopia therapy...

Background and study aims  Detection of polyps during colonoscopy is essential for screening colorectal cancer and computer-aided-diagnosis (CAD) could be helpful for this objective. The goal of this study was to assess the efficacy of CAD in detection of polyps in video colonoscopy by using three methods we have proposed and applied for diagnosis of polyps in wireless capsule colonoscopy. Patients and methods  Forty-two patients were included in the study, each one bearing one polyp. A dataset was generated with a total of 1680 polyp instances and 1360 frames of normal mucosa...