The impact of sound in people's behaviour in outdoor settings: A study using virtual reality and eye-tracking.

This paper presents an analysis of space perception and how visual cues, such as landmarks and sound, are perceived and impact people's behaviour while exploring a given outdoor space. The primary goal of the research is to investigate how auditory sensations and visual stimuli influence people's behaviour in outdoor built environments. Our technique compares people's perception of the built environment in different conditions: the real world and a replicated virtual world. As a case study, a university campus was used, and four experimental conditions were designed. The study followed a between-subjects design, and the data collection included gaze data acquired from an eye-tracking device as well as self-reports. The study concludes that sound influences human behaviour in such settings. More specifically conclusions are that: i) human behaviour in virtual replications of the real space, including both visual and sound stimuli, is tendentially more similar to human behaviour in the real world than in simulations omitting sound; and ii) there is a difference in human behaviour when people explore the same virtually replicated outdoor space, by varying the presence of sound. This study is particularly useful for researchers working on the comparison between human behaviour in virtual and real environments, related to visual and sound stimuli.

AI-Driven Decision Support for Early Detection of Cardiac Events: Unveiling Patterns and Predicting Myocardial Ischemia.

Cardiovascular diseases (CVDs) account for a significant portion of global mortality, emphasizing the need for effective strategies. This study focuses on myocardial infarction, pulmonary thromboembolism, and aortic stenosis, aiming to empower medical practitioners with tools for informed decision making and timely interventions. Drawing from data at Hospital Santa Maria, our approach combines exploratory data analysis (EDA) and predictive machine learning (ML) models, guided by the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. EDA reveals intricate patterns and relationships specific to cardiovascular diseases. ML models achieve accuracies above 80%, providing a 13 min window to predict myocardial ischemia incidents and intervene proactively. This paper presents a Proof of Concept for real-time data and predictive capabilities in enhancing medical strategies.

Multimodal user interfaces to improve social integration of elderly and mobility impaired.

Technologies for Human-Computer Interaction (HCI) and Communication have evolved tremendously over the past decades. However, citizens such as mobility impaired or elderly or others, still face many difficulties interacting with communication services, either due to HCI issues or intrinsic design problems with the services. In this paper we start by presenting the results of two user studies, the first one conducted with a group of mobility impaired users, comprising paraplegic and quadriplegic individuals; and the second one with elderly. The study participants carried out a set of tasks with a multimodal (speech, touch, gesture, keyboard and mouse) and multi-platform (mobile, desktop) system, offering an integrated access to communication and entertainment services, such as email, agenda, conferencing, instant messaging and social media, referred to as LHC - Living Home Center. The system was designed to take into account the requirements captured from these users, with the objective of evaluating if the adoption of multimodal interfaces for audio-visual communication and social media services, could improve the interaction with such services. Our study revealed that a multimodal prototype system, offering natural interaction modalities, especially supporting speech and touch, can in fact improve access to the presented services, contributing to the reduction of social isolation of mobility impaired, as well as elderly, and improving their digital inclusion.

Calcium Identification and Scoring Based on Echocardiography. An Exploratory Study on Aortic Valve Stenosis.

Currently, an echocardiography expert is needed to identify calcium in the aortic valve, and a cardiac CT-Scan image is needed for calcium quantification. When performing a CT-scan, the patient is subject to radiation, and therefore the number of CT-scans that can be performed should be limited, restricting the patient's monitoring. Computer Vision (CV) has opened new opportunities for improved efficiency when extracting knowledge from an image. Applying CV techniques on echocardiography imaging may reduce the medical workload for identifying the calcium and quantifying it, helping doctors to maintain a better tracking of their patients. In our approach, a simple technique to identify and extract the calcium pixel count from echocardiography imaging, was developed by using CV. Based on anonymized real patient echocardiographic images, this approach enables semi-automatic calcium identification. As the brightness of echocardiography images (with the highest intensity corresponding to calcium) vary depending on the acquisition settings, echocardiographic adaptive image binarization has been performed. Given that blood maintains the same intensity on echocardiographic images-being always the darker region-blood areas in the image were used to create an adaptive threshold for binarization. After binarization, the region of interest (ROI) with calcium, was interactively selected by an echocardiography expert and extracted, allowing us to compute a calcium pixel count, corresponding to the spatial amount of calcium. The results obtained from these experiments are encouraging. With this technique, from echocardiographic images collected for the same patient with different acquisition settings and different brightness, obtaining a calcium pixel count, where pixel values show an absolute pixel value margin of error of 3 (on a scale from 0 to 255), achieving a Pearson Correlation of 0.92 indicating a strong correlation with the human expert assessment of calcium area for the same images.

Detecting Nasal Vowels in Speech Interfaces Based on Surface Electromyography.

Nasality is a very important characteristic of several languages, European Portuguese being one of them. This paper addresses the challenge of nasality detection in surface electromyography (EMG) based speech interfaces. We explore the existence of useful information about the velum movement and also assess if muscles deeper down in the face and neck region can be measured using surface electrodes, and the best electrode location to do so. The procedure we adopted uses Real-Time Magnetic Resonance Imaging (RT-MRI), collected from a set of speakers, providing a method to interpret EMG data. By ensuring compatible data recording conditions, and proper time alignment between the EMG and the RT-MRI data, we are able to accurately estimate the time when the velum moves and the type of movement when a nasal vowel occurs. The combination of these two sources revealed interesting and distinct characteristics in the EMG signal when a nasal vowel is uttered, which motivated a classification experiment. Overall results of this experiment provide evidence that it is possible to detect velum movement using sensors positioned below the ear, between mastoid process and the mandible, in the upper neck region. In a frame-based classification scenario, error rates as low as 32.5% for all speakers and 23.4% for the best speaker have been achieved, for nasal vowel detection. This outcome stands as an encouraging result, fostering the grounds for deeper exploration of the proposed approach as a promising route to the development of an EMG-based speech interface for languages with strong nasal characteristics.