Acting Under Secretary of Health requested that a working group be established to develop the Veterans Health Initiative (VHI). He envisioned this as a comprehensive program to recognize the connection between certain health effects and military service, to allow military history to be better documented, to prepare health care providers to better serve their veteran patients, and to establish a data base for further study. This was first discussed by the Acting Under Secretary in relation to the health of former prisoners of war. Development was really begun by the former Chief Academic Affairs Officer, Dr. David Stevens, with the Military Service History project. This involves a pocket card for medical residents detailing the important components of a military service history targeting the health risks associated with various periods of service and more generic issues of concern and a website containing references relevant to the issues. Educational modules in the Veterans Health Initiative VHA will assist health care providers in recognizing the connection between certain health effects and military service, prepare health care providers to better serve veteran patients, and will provide a data base for further study. This independent study is designed to provide primary care practitioners with an introduction to the pathologies that lead to sight loss, their functional implications, appropriate method of referrals, training programs, and special considerations for interactions with visually impaired individuals. After completing this independent study, participants would be able to: • Define legal blindness; • Describe the causes of sight loss; • Delineate the functional implications of vision loss • Delineate the psycho/social impact of vision loss on the veteran; • Outline the role of the Visual Impairment Services Team (VIST) in the treatment of legally blind veterans and the referral process; • Describe the special personal and environmental considerations needed for visually impaired patients; • Describe the special medical considerations needed for visually impaired patients; • Describe the primary care practitioner's role in assisting veterans in establishing well-grounded claims for disability related to the loss of vision; and • Describe compensation and pension benefits provided for veterans with eye disabilities. After completing this independent study, you should 1. be able to: state the definition of legal blindness; 2. be able to: associate eye diseases with their visual implications; 3. be able to: demonstrate insight into the functional and Psycho/Social implications of sight-loss; 4. know when referrals to VIST are indicated; 5. understand …

시력 손상과 시각 장애(Visual Impairment and Blindness)

Affecting computing is an artificial intelligence area of study that recognizes, interprets, processes, and simulates human affects. The user's emotional states can be sensed through electroencephalography (EEG)-based Brain Computer Interfaces (BCI) devices. Research in emotion recognition using these tools is a rapidly growing field with multiple inter-disciplinary applications. This article performs a survey of the pertinent scientific literature from 2015 to 2020. It presents trends and a comparative analysis of algorithm applications in new implementations from a computer science perspective. Our survey gives an overview of datasets, emotion elicitation methods, feature extraction and selection, classification algorithms, and performance evaluation. Lastly, we provide insights for future developments.

EEG-Based BCI Emotion Recognition: A Survey.

This paper summarizes recent developments in audio and tactile 
feedback based assistive technologies targeting the blind 
community. Current technology allows applications to be 
efficiently distributed and run on mobile and handheld 
devices, even in cases where computational requirements are 
significant. As a result, electronic travel aids, navigational 
assistance modules, text-to-speech applications, as well as 
virtual audio displays which combine audio with haptic 
channels are becoming integrated into standard mobile devices. 
This trend, combined with the appearance of increasingly user-
friendly interfaces and modes of interaction has opened a 
variety of new perspectives for the rehabilitation and 
training of users with visual impairments. The goal of this 
paper is to provide an overview of these developments based on 
recent advances in basic research and application development. 
Using this overview as a foundation, an agenda is outlined for 
future research in mobile interaction design with respect to 
users with special needs, as well as ultimately in relation to 
sensor-bridging applications in general

/pdf/a-survey-of-assistive-technologies-and-applications-for-2kzqxgi4xs.pdf

A survey of assistive technologies and applications for blind users on mobile platforms: a review and foundation for research

Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deep-learning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deep-learning approach that we review are the input data type, deep-learning task, deep-learning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deep-learning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deep-learning concept perspective such as training architecture and dataset sources.

Deep Learning in Physiological Signal Data: A Survey.

Machine learning is an innovative approach that has extensive applications in prediction. This technique needs to be applied for the COVID-19 pandemic to identify patients at high risk, their death...

Significant applications of machine learning for covid-19 pandemic

This work was supported by the European Union’s Horizon 2020 Research and Innovative Programme under grant agreement no. 643636, “Sound of Vision”.

Designing sensory-substitution devices: Principles, pitfalls and potential1.

There has been steady progress in the field of affective computing over the last two decades that has integrated artificial intelligence techniques in the construction of computational models of emotion. Having, as a purpose, the development of a system for treating phobias that would automatically determine fear levels and adapt exposure intensity based on the user’s current affective state, we propose a comparative study between various machine and deep learning techniques (four deep neural network models, a stochastic configuration network, Support Vector Machine, Linear Discriminant Analysis, Random Forest and k-Nearest Neighbors), with and without feature selection, for recognizing and classifying fear levels based on the electroencephalogram (EEG) and peripheral data from the DEAP (Database for Emotion Analysis using Physiological signals) database. Fear was considered an emotion eliciting low valence, high arousal and low dominance. By dividing the ratings of valence/arousal/dominance emotion dimensions, we propose two paradigms for fear level estimation—the two-level (0—no fear and 1—fear) and the four-level (0—no fear, 1—low fear, 2—medium fear, 3—high fear) paradigms. Although all the methods provide good classification accuracies, the highest F scores have been obtained using the Random Forest Classifier—89.96% and 85.33% for the two-level and four-level fear evaluation modality.

https://www.mdpi.com/1424-8220/19/7/1738/pdf

Fear Level Classification Based on Emotional Dimensions and Machine Learning Techniques

Emotions constitute an indispensable component of our everyday life. They consist of conscious mental reactions towards objects or situations and are associated with various physiological, behavioral, and cognitive changes. In this paper, we propose a comparative analysis between different machine learning and deep learning techniques, with and without feature selection, for binarily classifying the six basic emotions, namely anger, disgust, fear, joy, sadness, and surprise, into two symmetrical categorical classes (emotion and no emotion), using the physiological recordings and subjective ratings of valence, arousal, and dominance from the DEAP (Dataset for Emotion Analysis using EEG, Physiological and Video Signals) database. The results showed that the maximum classification accuracies for each emotion were: anger: 98.02%, joy:100%, surprise: 96%, disgust: 95%, fear: 90.75%, and sadness: 90.08%. In the case of four emotions (anger, disgust, fear, and sadness), the classification accuracies were higher without feature selection. Our approach to emotion classification has future applicability in the field of affective computing, which includes all the methods used for the automatic assessment of emotions and their applications in healthcare, education, marketing, website personalization, recommender systems, video games, and social media.

/pdf/emotion-classification-based-on-biophysical-signals-and-4ui7dctcxn.pdf

Emotion Classification Based on Biophysical Signals and Machine Learning Techniques

Abstract For people who suffer from a visual impairment, navigating in an unknown environment represents a considerable challenge. To help them develop spatial orientation and mobility skills, researchers have employed audio-based computer games as a practical, interactive and user-centered learning approach. The highly immersive and attractive nature of audio games enables blind people to create a spatial representation of their environment that can be assigned to aid performing real-life navigation tasks. This paper reviews the most notable navigational audio-based games in terms of their conceptual and technological approach, accessibility and user interaction efficiency.

https://www.degruyter.com/downloadpdf/j/ijdhd.2015.14.issue-2/ijdhd-2014-0018/ijdhd-2014-0018.xml

Navigational audio games: an effective approach toward improving spatial contextual learning for blind people

In this paper, we investigate various machine learning classifiers used in our Virtual Reality (VR) system for treating acrophobia. The system automatically estimates fear level based on multimodal sensory data and a self-reported emotion assessment. There are two modalities of expressing fear ratings: the 2-choice scale, where 0 represents relaxation and 1 stands for fear; and the 4-choice scale, with the following correspondence: 0—relaxation, 1—low fear, 2—medium fear and 3—high fear. A set of features was extracted from the sensory signals using various metrics that quantify brain (electroencephalogram—EEG) and physiological linear and non-linear dynamics (Heart Rate—HR and Galvanic Skin Response—GSR). The novelty consists in the automatic adaptation of exposure scenario according to the subject’s affective state. We acquired data from acrophobic subjects who had undergone an in vivo pre-therapy exposure session, followed by a Virtual Reality therapy and an in vivo evaluation procedure. Various machine and deep learning classifiers were implemented and tested, with and without feature selection, in both a user-dependent and user-independent fashion. The results showed a very high cross-validation accuracy on the training set and good test accuracies, ranging from 42.5% to 89.5%. The most important features of fear level classification were GSR, HR and the values of the EEG in the beta frequency range. For determining the next exposure scenario, a dominant role was played by the target fear level, a parameter computed by taking into account the patient’s estimated fear level.

Oana Balan

Papers

Designing sensory-substitution devices: Principles, pitfalls and potential1.

Fear Level Classification Based on Emotional Dimensions and Machine Learning Techniques

Emotion Classification Based on Biophysical Signals and Machine Learning Techniques

Navigational audio games: an effective approach toward improving spatial contextual learning for blind people

An Investigation of Various Machine and Deep Learning Techniques Applied in Automatic Fear Level Detection and Acrophobia Virtual Therapy.