scholarly journals Simulation-Based Cryosurgery Training

2016 ◽  
Vol 15 (6) ◽  
pp. 805-814 ◽  
Author(s):  
Anjali Sehrawat ◽  
Robert Keelan ◽  
Kenji Shimada ◽  
Dona M. Wilfong ◽  
James T. McCormick ◽  
...  
Keyword(s):  
Performance Improvement ◽  
System Development ◽  
Training Session ◽  
Geometric Constraints ◽  
Training Data ◽  
Surgical Resident ◽  
Insertion Depth ◽  
Target Region ◽  
Advanced Level ◽  
Training Tool

A proof-of-concept for an advanced-level computerized training tool for cryosurgery is demonstrated, based on three-dimensional cryosurgery simulations and a variable insertion depth strategy for cryoprobes. The objective for system development is two-fold: to identify a cryoprobe layout in order to best match a planning isotherm with the target region shape and to verify that cryoprobe placement does not violate accepted geometric constraints. System validation has been performed by collecting training data from 17 surgical residents having no prior experience or advanced knowledge of cryosurgery. This advanced-level study includes an improved training session design in order to enhance knowledge dissemination and elevate participant motivation to excel. In terms of match between a planning isotherm and the target region shape, results of this demonstrate trainee performance improvement from 4.4% in a pretest to 44.4% in a posttest over a course of 50 minutes of training. In terms of combined performance, including the above-mentioned geometrical match and constraints on cryoprobe placement, this study demonstrates trainee performance improvement from 2.2% in the pretest to 31.1% in the posttest. Given the relatively short training session and the lack of prior knowledge, these improvements are significant and encouraging. These results are of particular significance, as they have been obtained from a surgical resident population which are exposed to the typical stress and constraints in advanced surgical education.

172 Scenario-based Training Is an Effective Tactic for Teaching Applied Livestock Management Practices to Extension Personnel

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 92-92
Author(s):  
Jennie L Ivey ◽  
Lew G Strickland ◽  
Justin D Rhinehart
Keyword(s):  
Management Practices ◽  
Teaching Effectiveness ◽  
Training Session ◽  
Real Life ◽  
Extension Agents ◽  
Adult Educator ◽  
Topic Area ◽  
Virtual Training ◽  
Training Tool ◽  
Instructor Effectiveness

Abstract Developing livestock and equine trainings to empower county Extension agents is challenging, especially when spanning in-person and online delivery modules. Real life application of training concepts is difficult, particularly when participants have varied backgrounds and experience. Thus, we assessed if scenario-based training modules were an effective training method across in-person and virtual formats. The same scenario-based training was delivered at three, regional in-person trainings (n = 42), and one virtual training (n = 32). Training format consisted of four, species-specific lectures addressing various production topics. Small groups then developed recommendations for a specific scenario, followed by a debriefing session consisting of group reactions and specialist recommendations. Topic-area application to county programs, instructor effectiveness, and overall benefit of the training session were evaluated (Qualtrics, in-person n = 26, 62% completion; virtual n = 17, 53% completion). Data were assessed using analysis of variance and mean comparisons (α=0.05), with Tukey’s pairwise post hoc analysis where appropriate (STATA 16). Across all sessions, likert scale responses (1=poor and 5=excellent, n = 43) indicated lecture sessions were applicable to county areas of need across material content (mean±SD, cattle=4.71±0.57, equine=4.64±0.50), teaching effectiveness (cattle=4.77±0.42, equine=4.75±0.43), and overall quality (cattle=4.68±0.57, equine=4.67±0.51), respectively. Scenario-based training benefit was not influenced by the number of times an agent had attended in-service training on livestock species, agent appointment (youth vs. adult educator), or training location (p >0.05). Attendance at previous in-service trainings (cattle P = 0.005; equine P = 0.013) and agent appointment (cattle P = 0.0006; equine P = 0.05) had a significant impact on the number of questions agents reported to have received on scenario topics in the last 12 months. More topic area questions were reported by agents with adult education responsibilities and previous training attendance. Based upon these results, scenario-based training is an effective in-person and virtual training tool for 4-H and adult Extension agents of varying experience.

Consolidation Effects in a Full-Body Diving Task

2017 ◽  
Vol 5 (2) ◽  
pp. 291-303
Author(s):  
Maxime Trempe ◽  
Jean-Luc Gohier ◽  
Mathieu Charbonneau ◽  
Jonathan Tremblay
Keyword(s):  
Skill Acquisition ◽  
Training Session ◽  
Video Camera ◽  
The Body ◽  
Initial Training ◽  
Body Movements ◽  
Training Tool ◽  
Significant Performance ◽  
The Moment ◽  
Full Body

In recent years, it has been shown that spacing training sessions by several hours allows the consolidation of motor skills in the brain, a process leading to the stabilization of the skills and, sometimes, further improvement without additional practice. At the moment, it is unknown whether consolidation can lead to an improvement in performance when the learner performs complex full-body movements. To explore this question, we recruited 10 divers and had them practice a challenging diving maneuver. Divers first performed an initial training session, consisting of 12 dives during which visual feedback was provided immediately after each dive through video replay. Two retention tests without feedback were performed 30 min and 24 hr after the initial training session. All dives were recorded using a video camera and the participants’ performance was assessed by measuring the verticality of the body segments at water entry. Significant performance gains were observed in the 24-hr retention test (p < .05). These results suggest that the learning of complex full-body movements can benefit from consolidation and that splitting practice sessions can be used as a training tool to facilitate skill acquisition.

Incremental Learning-Based Facial Expression Classification System Using a Novel Multinomial Classifier

2017 ◽  
Vol 32 (04) ◽  
pp. 1856004 ◽  
Author(s):  
M. Sultan Zia ◽  
Majid Hussain ◽  
M. Arfan Jaffar
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Incremental Learning ◽  
Facial Expression Recognition ◽  
Expression Patterns ◽  
Training Session ◽  
Recognition System ◽  
High Accuracy ◽  
Training Data ◽  
Expression Recognition

Facial expressions recognition is a crucial task in pattern recognition and it becomes even crucial when cross-cultural emotions are encountered. Various studies in the past have shown that all the facial expressions are not innate and universal, but many of them are learned and culture-dependent. Extreme facial expression recognition methods employ different datasets for training and later use it for testing and demostrate high accuracy in recognition. Their performances degrade drastically when expression images are taken from different cultures. Moreover, there are many existing facial expression patterns which cannot be generated and used as training data in single training session. A facial expression recognition system can maintain its high accuracy and robustness globally and for a longer period if the system possesses the ability to learn incrementally. We also propose a novel classification algorithm for multinomial classification problems. It is an efficient classifier and can be a good choice for base classifier in real-time applications. We propose a facial expression recognition system that can learn incrementally. We use Local Binary Pattern (LBP) features to represent the expression space. The performance of the system is tested on static images from six different databases containing expressions from various cultures. The experiments using the incremental learning classification demonstrate promising results.

scholarly journals A LEARNING METHOD TO DETERMINE HOW TO APPROACH AN UNKNOWN OBJECT TO BE GRASPED

2011 ◽  
Vol 08 (03) ◽  
pp. 579-606 ◽  
Author(s):  
BENJAMIN D. BALAGUER ◽  
STEFANO CARPIN
Keyword(s):  
Learning Algorithm ◽  
Training Session ◽  
Training Data ◽  
Support Vector ◽  
The Novel ◽  
Learning Method ◽  
End Effector ◽  
Novel Object ◽  
Time Performance ◽  
Feature Based

We present a learning algorithm to determine the appropriate approaching pose to grasp a novel object. Our method focuses on the computation of valid end-effector orientations in order to make contact with the object at a given point. The system achieves this goal by generalizing from positive examples provided by a human operator during an offline training session. The technique is feature-based since it extracts salient attributes of the object to be grasped rather than relying on the availability of models or trying to build one. To compute the desired orientation, the robot performs three steps at run time. Using a multi-class Support Vector Machine (SVM), it first classifies the novel object into one of the object classes defined during training. Next, it determines its orientation, and, finally, based on the classification and orientation, it extracts the most similar example from the training data and uses it to grasp the object. The method has been implemented on a full-scale humanoid robotic torso equipped with multi-fingered hands and extensive results corroborate both its effectiveness and real-time performance.

scholarly journals Probing the Effect of Selection Bias on Generalization: A Thought Experiment

2021 ◽  
Author(s):  
John Tsotsos ◽  
Jun Luo
Keyword(s):  
Data Collection ◽  
Selection Bias ◽  
Visual Recognition ◽  
Thought Experiment ◽  
System Development ◽  
Training Data ◽  
Real Problem ◽  
Data Sets ◽  
Training Set ◽  
Unseen Data

Abstract Learned systems in the domain of visual recognition and cognition impress in part because even though they are trained with datasets many orders of magnitude smaller than the full population of possible images, they exhibit sufficient generalization to be applicable to new and previously unseen data. Since training data sets typically represent such a small sampling of any domain, the possibility of bias in their composition is very real. But what are the limits of generalization given such bias, and up to what point might it be sufficient for a real problem task? Although many have examined issues regarding generalization from several perspectives, this question may require examining the data itself. Here, we focus on the characteristics of the training data that may play a role. Other disciplines have grappled with these problems also, most interestingly epidemiology, where experimental bias is a critical concern. The range and nature of data biases seen clinically are really quite relatable to learned vision systems. One obvious way to deal with bias is to ensure a large enough training set, but this might be infeasible for many domains. Another approach might be to perform a statistical analysis of the actual training set, to determine if all aspects of the domain are fairly captured. This too is difficult, in part because the full set of important variables might not be known, or perhaps not even knowable. Here, we try a different, simpler, approach in the tradition of the Thought Experiment, whose most famous instance is perhaps Schrödinger's Cat, to address part of these problems. There are many types of bias as will be seen, but we focus only on one, selection bias. The point of the thought experiment is not to demonstrate problems with all learned systems. Rather, this might be a simple theoretical tool to probe into bias during data collection to highlight deficiencies that might then deserve extra attention either in data collection or system development.

scholarly journals MEDREV: feasibility study of a combined pharmacy–health psychology intervention to improve care for people with dementia with behaviour that challenges in care homes

2019 ◽  
Vol 69 (suppl 1) ◽  
pp. bjgp19X703253
Author(s):  
Ian Maidment
Keyword(s):  
Feasibility Study ◽  
Qualitative Interviews ◽  
Care Home ◽  
Training Session ◽  
Dementia Care ◽  
Training Data ◽  
Care Staff ◽  
Care Homes ◽  
Inappropriate Use ◽  
People With Dementia

Background‘Behaviour that Challenges’ is common in older people with dementia in care homes and treated with antipsychotics. Policy is focused on reducing the use of antipsychotics in people with dementia and therefore reducing harm. This submission reports results on a NIHR-funded feasibility study MEDREV.AimTo assess the feasibility of medication review by a specialist dementia care pharmacist combined with staff training with the objective of limiting the inappropriate use of psychotropics.MethodCare homes were recruited. People meeting the inclusion (dementia; medication for behaviour that challenges), or their personal consultee, were approached. A specialist dementia care pharmacist reviewed medication and made recommendations. Care staff received a 3-hour training session promoting person-centred care and GPs’ brief training. Data were collected on recruitment and retention, and implementation of recommendations. Other outcomes included the Neuropsychiatric Inventory-Nursing Home version, quality of life (EQ-5D/DEMQoL), cognition (sMMSE), and health economic (CSRI). Qualitative interviews explored expectations and experiences.ResultsMedication reviews were conducted in 29 of 34 residents recruited and the pharmacist recommended reviewing medication in 21 of these. Fifteen (71.4%) of these were antidepressants: 57.1% (12 of 21) of recommendations were implemented and implementation took a mean of 98.4 days. Non-implementation themes for will be presented. One hundred and sixty-four care staff received training (care homes = 142; primary care = 22). Twenty-one participants (care home managers = 5; GPs = 3; nurses = 2; care staff = 11) were interviewed.ConclusionThe study was feasible, although the approach would need modification to improve the uptake of reviews and reduce the delay in implementation. Most of the recommendations related to antidepressants.

scholarly journals Changes in Physical Fitness of Older People During the COVID-19 Pandemic in Germany: Objective Training Data

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 460-460
Author(s):  
Jessica Koschate ◽  
Michel Hackbarth ◽  
Sandra Lau ◽  
Tania Zieschang
Keyword(s):  
Training Session ◽  
Training Data ◽  
Initial Training ◽  
Muscle Training ◽  
Adverse Health Effects ◽  
Before And After ◽  
Baseline Levels ◽  
Germany Objective ◽  
Individual Training

Abstract The purpose of this study was to analyze objective training data on changes in leg muscle training before and after the COVID-19 lockdown during spring 2020 in Germany. Overall, the training data of 4435 individuals in the age group (AG) 45-64 years (55±5 years, 66% ♀) and of 2853 in the AG 65-95 years (72±6 years, 54% ♀) were exported from chip-controlled exercise circuits. Training weight and number of repetitions performed on the leg extensor were used to calculate a leg score (LS), considering the last three training sessions before the lockdown (baseline) and the first ten individual sessions as well as the averaged sessions for August, September and October after individual training resumption. Based on the baseline LS, three training intensity groups (TG_low, medium, high) were defined, and analyzed for differences (ANOVA). The LS in TG_low remained stable after the lockdown, but increased compared to baseline in both AGs after the first ten sessions (p&lt;0.05). In TG_medium, LS was reduced at the first post training session (p&lt;0.05) and returned to baseline levels at training session eight in the younger and session two in the older adults. In both AGs, LS was reduced in the TG_high (p&lt;0.001), and did not reach baseline levels by October. Hence, the LS of TG_high was identified as being particularly affected by the training interruption, irrespective of age. More individually tailored training recommendations should be made for these individuals to be able to regain their initial training levels and avoid long-term adverse health effects.

scholarly journals Two-stage Training for Learning from Label Proportions

2021 ◽  
Author(s):  
Jiabin Liu ◽  
Bo Wang ◽  
Xin Shen ◽  
Zhiquan Qi ◽  
Yingjie Tian
Keyword(s):  
Performance Improvement ◽  
Training Data ◽  
Continuous Training ◽  
High Entropy ◽  
Training Stage ◽  
Probabilistic Classifier ◽  
Leibler Divergence ◽  
Learning From Label Proportions ◽  
Post Hoc ◽  
The Given

Learning from label proportions (LLP) aims at learning an instance-level classifier with label proportions in grouped training data. Existing deep learning based LLP methods utilize end-to-end pipelines to obtain the proportional loss with Kullback-Leibler divergence between the bag-level prior and posterior class distributions. However, the unconstrained optimization on this objective can hardly reach a solution in accordance with the given proportions. Besides, concerning the probabilistic classifier, this strategy unavoidably results in high-entropy conditional class distributions at the instance level. These issues further degrade the performance of the instance-level classification. In this paper, we regard these problems as noisy pseudo labeling, and instead impose the strict proportion consistency on the classifier with a constrained optimization as a continuous training stage for existing LLP classifiers. In addition, we introduce the mixup strategy and symmetric cross-entropy to further reduce the label noise. Our framework is model-agnostic, and demonstrates compelling performance improvement in extensive experiments, when incorporated into other deep LLP models as a post-hoc phase.

scholarly journals The use and effectiveness of cadaveric workshops in higher surgical training: a systematic review

2011 ◽  
Vol 93 (5) ◽  
pp. 347-352 ◽  
Author(s):  
J Gilbody ◽  
AW Prasthofer ◽  
K Ho ◽  
ML Costa
Keyword(s):  
Systematic Review ◽  
Surgical Training ◽  
English Language ◽  
Training Session ◽  
Skills Training ◽  
Cochrane Library ◽  
Eligibility Criteria ◽  
Training Tool ◽  
Emergency Procedures ◽  
Surgical Trainees

INTRODUCTION The aim of this systematic review is to describe the use of cadavers in postgraduate surgical training, to determine the effect of cadaveric training sessions on surgical trainees' technical skills performance and to determine how trainees perceive the use of cadaveric workshops as a training tool. METHODS An electronic literature search was performed, restricted to the English language, of MEDLINE®, Embase™, the Cumulative Index to Nursing and Allied Health Literature (CINAHL®), Centre for Agricultural Bioscience (CAB) Abstracts, the Educational Resources Information Center (ERIC™) database, the British Education Index, the Australian Education Index, the Cochrane Library and the Best Evidence in Medical Education website. Studies that were eligible for review included primary studies evaluating the use of human cadaveric surgical workshops for surgical skills training in postgraduate surgical trainees and those that included a formal assessment of skills performance or trainee satisfaction after the training session. RESULTS Eight studies were identified as satisfying the eligibility criteria. One study showed a benefit from cadaveric workshop training with regard to the ability of trainees to perform relatively simple emergency procedures and one showed weak evidence of a benefit in performing more complex surgical procedures. Three studies showed that trainees valued the experience of cadaveric training. CONCLUSIONS Evidence for the effectiveness of cadaveric workshops in surgical training is currently limited. In particular, there is little research into how these workshops improve the performance of surgical trainees during subsequent live surgery. However, both trainees and assessors hold them in high regard and feel they help to improve operative skills. Further research into the role of cadaveric workshops is required.

scholarly journals Tangent Space Features-Based Transfer Learning Classification Model for Two-Class Motor Imagery Brain–Computer Interface

2019 ◽  
Vol 29 (10) ◽  
pp. 1950025 ◽  
Author(s):  
Pramod Gaur ◽  
Karl McCreadie ◽  
Ram Bilas Pachori ◽  
Hui Wang ◽  
Girijesh Prasad
Keyword(s):  
Transfer Learning ◽  
Motor Imagery ◽  
Tangent Space ◽  
Brain Computer Interface ◽  
Training Session ◽  
Training Data ◽  
Classification Model ◽  
Computer Interface ◽  
Multivariate Empirical Mode Decomposition ◽  
Subject Specific

The performance of a brain–computer interface (BCI) will generally improve by increasing the volume of training data on which it is trained. However, a classifier’s generalization ability is often negatively affected when highly non-stationary data are collected across both sessions and subjects. The aim of this work is to reduce the long calibration time in BCI systems by proposing a transfer learning model which can be used for evaluating unseen single trials for a subject without the need for training session data. A method is proposed which combines a generalization of the previously proposed subject-specific “multivariate empirical-mode decomposition” preprocessing technique by taking a fixed band of 8–30[Formula: see text]Hz for all four motor imagery tasks and a novel classification model which exploits the structure of tangent space features drawn from the Riemannian geometry framework, that is shared among the training data of multiple sessions and subjects. Results demonstrate comparable performance improvement across multiple subjects without subject-specific calibration, when compared with other state-of-the-art techniques.

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