Electrical stimulation for neuromuscular testing and training: state-of-the art and unresolved issues

With recent advances in technologies, deep learning is being applied more and more to different tasks. In particular, point cloud processing and classification have been studied for a while now, with various methods developed. Some of the available classification approaches are based on specific data source, like LiDAR, while others are focused on specific scenarios, like indoor. A general major issue is the computational efficiency (in terms of power consumption, memory requirement, and training/inference time). In this study, we propose an efficient framework (named TONIC) that can work with any kind of aerial data source (LiDAR or photogrammetry) and does not require high computational power while achieving accuracy on par with the current state of the art methods. We also test our framework for its generalization ability, showing capabilities to learn from one dataset and predict on unseen aerial scenarios.

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Body and health in the training of health professionals: a mapping of scientific production

JOURNAL OF RESEARCH AND KNOWLEDGE SPREADING ◽

10.20952/jrks2112480 ◽

2021 ◽

Vol 2 (1) ◽

Author(s):

Márcia Cristina Rocha Paranhos ◽

Lívia De Rezende Cardoso

Keyword(s):

Health Professionals ◽

Professional Training ◽

State Of The Art ◽

National Curriculum ◽

Health Curriculum ◽

Academic Production ◽

Unified Health System ◽

Curriculum Guidelines ◽

Theses And Dissertations ◽

And Training

This article builds a mapping in order to analyze the theses and dissertations about body, health, curriculum and training of health professionals. For this, theses and dissertations were mapped in the period from 2010 to 2020 through a state-of-the-art study. The composition of the data is given by the presentation and discussion of the listed texts. As for research, these concern the production of bodies based on biotechnological discourses; professional training in health; others point to the curricula of health courses after the National Curriculum Guidelines (DCN); the performance of health professionals in relation to the Unified Health System (SUS); teaching strategies for health training; corporeidity in the curricula, especially in the curricula of the Physical Education course; the anatomoclinical body and educational health practices. In this perspective, some contributions, limits and possibilities of this academic production were observed.

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Using a virtual machine environment for developing, testing, and training for the UM-UKCA composition-climate model, using Unified Model version 10.9 and above

Geoscientific Model Development ◽

10.5194/gmd-11-3647-2018 ◽

2018 ◽

Vol 11 (9) ◽

pp. 3647-3657 ◽

Cited By ~ 1

Author(s):

Nathan Luke Abraham ◽

Alexander T. Archibald ◽

Paul Cresswell ◽

Sam Cusworth ◽

Mohit Dalvi ◽

...

Keyword(s):

Virtual Machine ◽

Climate Model ◽

State Of The Art ◽

Model Development ◽

Unified Model ◽

Model Version ◽

Weather And Climate ◽

Interactive Composition ◽

Comprehensive Test ◽

And Training

Abstract. The Met Office Unified Model (UM) is a state-of-the-art weather and climate model that is used operationally worldwide. UKCA is the chemistry and aerosol sub model of the UM that enables interactive composition and physical atmosphere interactions, but which adds an additional 120 000 lines of code to the model. Ensuring that the UM code and UM-UKCA (the UM running with interactive chemistry and aerosols) is well tested is thus essential. While a comprehensive test harness is in place at the Met Office and partner sites to aid in development, this is not available to many UM users. Recently, the Met Office have made available a virtual machine environment that can be used to run the UM on a desktop or laptop PC. Here we describe the development of a UM-UKCA configuration that is able to run within this virtual machine while only needing 6 GB of memory, before discussing the applications of this system for model development, testing, and training.

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Reviewing Autoencoders for Missing Data Imputation: Technical Trends, Applications and Outcomes

Journal of Artificial Intelligence Research ◽

10.1613/jair.1.12312 ◽

2020 ◽

Vol 69 ◽

pp. 1255-1285

Author(s):

Ricardo Cardoso Pereira ◽

Miriam Seoane Santos ◽

Pedro Pereira Rodrigues ◽

Pedro Henriques Abreu

Keyword(s):

Missing Data ◽

Missing Values ◽

State Of The Art ◽

Data Imputation ◽

Tabular Data ◽

Missing Data Imputation ◽

Learning Techniques ◽

Real World Datasets ◽

And Training ◽

Machine Learning Models

Missing data is a problem often found in real-world datasets and it can degrade the performance of most machine learning models. Several deep learning techniques have been used to address this issue, and one of them is the Autoencoder and its Denoising and Variational variants. These models are able to learn a representation of the data with missing values and generate plausible new ones to replace them. This study surveys the use of Autoencoders for the imputation of tabular data and considers 26 works published between 2014 and 2020. The analysis is mainly focused on discussing patterns and recommendations for the architecture, hyperparameters and training settings of the network, while providing a detailed discussion of the results obtained by Autoencoders when compared to other state-of-the-art methods, and of the data contexts where they have been applied. The conclusions include a set of recommendations for the technical settings of the network, and show that Denoising Autoencoders outperform their competitors, particularly the often used statistical methods.

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Seven Well Known Fundamental Flaws against Innovations in Construction Chemistry

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.466.15 ◽

2011 ◽

Vol 466 ◽

pp. 15-19 ◽

Cited By ~ 1

Author(s):

Peter Seidler

Keyword(s):

State Of The Art ◽

New Products ◽

Education And Training ◽

The State ◽

Actual Knowledge ◽

Efficient Organization ◽

Improve Efficiency ◽

And Training

Construction chemistry is underdeveloped compared to other chemical branches. Innovation is realized by new products, improved pro¬cesses or / and more efficient organization. Innovation becomes evident when a noticeable progress is achieved by implementing changes. There are seven fundamental hindrances or flaws possible which are briefly considered. The state-of-the-art must be known. Innovation is measured in comparison to this state-of-the-art. If this level is not yet attained, progress is easily realized by introducing the actual knowledge. The realization is measured according to qualitative or preferably quantitative bench¬marks. Unfortunately, this is not currently done in the field of construction chemistry. Before benchmarking starts, communication based on truth and trust must be effective. The available scientific me¬tho¬dology must be known. Benchmarking will possibly show deficiencies in education and training. This will stress the need for adequate trans¬parency to improve efficiency. Hope¬ful¬ly, a self-regulating process improving pro-ducts and processes will be created in this way.

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Hybrid Exoskeletons for Upper Limb Stroke Rehabilitation

Handbook of Research on Biomimetics and Biomedical Robotics - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-2993-4.ch004 ◽

2018 ◽

pp. 76-98

Author(s):

Ashley M. Stewart ◽

Christopher G. Pretty ◽

Mark Adams ◽

XiaoQi Chen

Keyword(s):

Electrical Stimulation ◽

Upper Limb ◽

Functional Electrical Stimulation ◽

Stroke Rehabilitation ◽

Potential Benefit ◽

Patient Monitoring ◽

State Of The Art ◽

Early Stage ◽

Control Methods ◽

Stage Of Development

Hybrid exoskeletons are a recent development, combining electrically controlled actuation with functional electrical stimulation, which potentially offers great benefits for muscular rehabilitation. This chapter presents a review on the state of the art of upper-limb hybrid exoskeletons with a particular focus on stroke rehabilitation. The current needs of the stroke rehabilitation field are discussed and the ability of hybrid exoskeletons to provide a solution to some of the gaps in this field is explored. Due to the early stage of development which most hybrid exoskeletons are in, little research has yet been done in control methods used for them. In particular, more investigation is needed with regards to the potential benefit of hybrid exoskeletons as a patient-monitoring and rehabilitation assist-as-need tool.

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Innovative Tools and Models for E-Learning in Romania

Learning with Mobile Technologies, Handheld Devices, and Smart Phones ◽

10.4018/978-1-4666-0936-5.ch007 ◽

2012 ◽

pp. 105-122

Author(s):

Liviu Moldovan

Keyword(s):

Vocational Education ◽

Digital Media ◽

Vocational Training ◽

State Of The Art ◽

Vocational Education And Training ◽

High Definition ◽

Training Quality ◽

E Learning ◽

Pedagogical Innovations ◽

And Training

This article reports examples from new, ongoing distance learning activities in Romania that utilize state of the art digital media, tools, and methods. Examples include state of the art video tools, design of video infrastructure, and training courses employed for classroom modernisation, to address technological and pedagogical innovations in vocational education and training. The objective is to renovate the teaching infrastructure used by specialists in vocational education, and improve vocational training quality by providing more flexible trainings paths to the Romanian labor market. The latter includes dissemination of a new model for organizing and delivering professional vocational training comprising of competence transfer, competence export, building networks, and development of contacts with vocational schools within a regional development perspective. The training delivery utilizes state of the art ICT solutions, high definition video services, and blended learning frameworks.

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An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i01.5364 ◽

2020 ◽

Vol 34 (01) ◽

pp. 303-311 ◽

Cited By ~ 3

Author(s):

Sicheng Zhao ◽

Yunsheng Ma ◽

Yang Gu ◽

Jufeng Yang ◽

Tengfei Xing ◽

...

Keyword(s):

Neural Networks ◽

Emotion Recognition ◽

State Of The Art ◽

Source Code ◽

Cross Entropy ◽

Attention Network ◽

Audio Features ◽

End To End ◽

3D Cnn ◽

And Training

Emotion recognition in user-generated videos plays an important role in human-centered computing. Existing methods mainly employ traditional two-stage shallow pipeline, i.e. extracting visual and/or audio features and training classifiers. In this paper, we propose to recognize video emotions in an end-to-end manner based on convolutional neural networks (CNNs). Specifically, we develop a deep Visual-Audio Attention Network (VAANet), a novel architecture that integrates spatial, channel-wise, and temporal attentions into a visual 3D CNN and temporal attentions into an audio 2D CNN. Further, we design a special classification loss, i.e. polarity-consistent cross-entropy loss, based on the polarity-emotion hierarchy constraint to guide the attention generation. Extensive experiments conducted on the challenging VideoEmotion-8 and Ekman-6 datasets demonstrate that the proposed VAANet outperforms the state-of-the-art approaches for video emotion recognition. Our source code is released at: https://github.com/maysonma/VAANet.

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