A review of sonification solutions in assistive systems for visually impaired people.

Purpose: Visually impaired people (VIP) find it challenging to understand and gain awareness of their surroundings. Most activities require the use of the auditory or tactile senses. As such, assistive systems which are capable of aiding visually impaired people to understand, navigate and form a mental representation of their environment are extensively being studied and developed.

Multispectral Imaging for Skin Diseases Assessment-State of the Art and Perspectives.

Skin optical inspection is an imperative procedure for a suspicious dermal lesion since very early skin cancer detection can guarantee total recovery. Dermoscopy, confocal laser scanning microscopy, optical coherence tomography, multispectral imaging, multiphoton laser imaging, and 3D topography are the most outstanding optical techniques implemented for skin examination. The accuracy of dermatological diagnoses attained by each of those methods is still debatable, and only dermoscopy is frequently used by all dermatologists.

CultReal-A Rapid Development Platform for AR Cultural Spaces, with Fused Localization.

Virtual and augmented reality technologies have known an impressive market evolution due to their potential to provide immersive experiences. However, they still have significant difficulties to enable fully fledged, consumer-ready applications that can handle complex tasks such as multi-user collaboration or time-persistent experiences. In this context, CultReal is a rapid creation and deployment platform for augmented reality (AR), aiming to revitalize cultural spaces.

Machine Learning Methods for Fear Classification Based on Physiological Features.

This paper focuses on the binary classification of the emotion of fear, based on the physiological data and subjective responses stored in the DEAP dataset. We performed a mapping between the discrete and dimensional emotional information considering the participants' ratings and extracted a substantial set of 40 types of features from the physiological data, which represented the input to various machine learning algorithms-Decision Trees, k-Nearest Neighbors, Support Vector Machine and artificial networks-accompanied by dimensionality reduction, feature selection and the tuning of the most relevant hyperparameters, boosting classification accuracy. The methodology we approached included tackling different situations, such as resolving the problem of having an imbalanced dataset through data augmentation, reducing overfitting, computing various metrics in order to obtain the most reliable classification scores and applying the Local Interpretable Model-Agnostic Explanations method for interpretation and for explaining predictions in a human-understandable manner.

Semi-automatic vessel detection for challenging cases of peripheral arterial disease.

Objectives: Manual or semi-automated segmentation of the lower extremity arterial tree in patients with Peripheral arterial disease (PAD) remains a notoriously difficult and time-consuming task. The complex manifestations of the disease, including discontinuities of the vascular flow channels, the presence of calcified atherosclerotic plaque in close vicinity to adjacent bone, and the presence of metal or other imaging artifacts currently preclude fully automated vessel identification. New machine learning techniques may alleviate this challenge, but require large and reasonably well segmented training data.

Integrating Biosignals Measurement in Virtual Reality Environments for Anxiety Detection.

This paper proposes a protocol for the acquisition and processing of biophysical signals in virtual reality applications, particularly in phobia therapy experiments. This protocol aims to ensure that the measurement and processing phases are performed effectively, to obtain clean data that can be used to estimate the users' anxiety levels. The protocol has been designed after analyzing the experimental data of seven subjects who have been exposed to heights in a virtual reality environment.

Flexible Virtual Reality System for Neurorehabilitation and Quality of Life Improvement.

As life expectancy is mostly increasing, the incidence of many neurological disorders is also constantly growing. For improving the physical functions affected by a neurological disorder, rehabilitation procedures are mandatory, and they must be performed regularly. Unfortunately, neurorehabilitation procedures have disadvantages in terms of costs, accessibility and a lack of therapists.

A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision.

Computer vision based indoor localization methods use either an infrastructure of static cameras to track mobile entities (e.g., people, robots) or cameras attached to the mobile entities.

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

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.

Fear Level Classification Based on Emotional Dimensions and Machine Learning Techniques.

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.

Patient autonomy and disclosure of material information about hospital-acquired infections.

Hospital-acquired infections are nowadays a major health care problem worldwide. The morbidity and mortality associated with them are highest in intensive care units, but their effects are identifiable in virtually any medical department. Information about hospital-acquired infections, especially about their preventive measures, are rarely presented nowadays in a correct fashion to patients.

Biometric identity management for standard mobile medical networks.

The explosion of healthcare costs over the last decade has prompted the ICT industry to respond with solutions for reducing costs while improving healthcare quality. The ISO/IEEE 11073 family of standards recently released is the first step towards interoperability of mobile medical devices used in patient environments. The standards do not, however, tackle security problems, such as identity management, or the secure exchange of medical data.