Abstract
Peripheral arterial disease (PAD) causes blockage of the arteries, altering the blood flow to the lower limbs. This blockage can cause the individual with PAD to feel severe pain in the lower limbs. The main contribution of this research is the discovery of a solution that allows the automatic detection of the onset of claudication based on data analysis from patients' smartphones. For the data-collection procedure, 40 patients were asked to walk with a smartphone on a thirty-meter path, back and forth, for six minutes. Each patient conducted the test twice on two different days. Several machine learning models were compared to detect the onset of claudication on two different datasets. The results suggest that we can identify the onset of claudication using inertial sensors with a best case accuracy of 92.25% for the Extreme Gradient Boosting model.

Abstract
O Grupo Internacional de Pesquisa Educação Digital (GPe-dU – UNISINOS/CAPES-CNPq)1 iniciou, em 2016, a pesquisa intitulada “A Cidade como Espaço de Aprendizagem: games e gamificação na constituição de Espaços de Convivência Híbridos, Multimodais, Pervasivos e Ubíquos para o desenvolvimento da Cidadania”, financiada pelo Edital Universal, na qual teve origem o We, Learning with the Cibricity – WLC. O evento foi construído a partir da necessidade da criação de um espaço-tempo comum de convivência e compartilhamento dos games e projetos gamificados desenvolvidos pelas crianças e adolescentes na cidade, bem como das práticas pedagógicas desenvolvidas pelos professores, realizadas nas escolas participantes do projeto, ao longo dos anos letivos em que o projeto se desenvolveu. Assim, o WLC foi realizado em 2016 seguido por quatro edições, até o ano de 2019, na modalidade presencial física. A pesquisa teve continuidade no projeto “A Cibricidade como Espaço de Aprendizagem: Pra´ticas pedago´gicas inovadoras para a promoc¸a~o da cidadania e do desenvolvimento social sustentável”, financiado pelo Edital “Anos finais do Ensino Fundamental: adolescências, qualidade e equidade na escola pública” da Fundação Carlos Chagas e Itaú Social, desenvolvido nos anos de 2019 a 2022. A articulação entre pesquisa, ensino (superior, pós-graduação e educação básica) e extensão (formação continuada de professores e a organização e realização do evento WLC) tomou tal dimensão, que fez surgir oportunidades de compartilhamento de projetos realizados também pelos professores, em um profícuo diálogo com pesquisadores, bem como a ampliação geográfica dos participantes, extrapolando as fronteiras nacionais. Essa história está sendo construída a partir das diferentes temáticas escolhidas e propostas em conexão com o que vivemos com o outro, sendo esse outro e entendido como tudo que faz parte da nossa ecologia.

Abstract
To diagnose mobility impairments and select appropriate physiotherapy, gait assessment studies are often recommended. These studies are usually conducted in confined clinical settings, which may feel foreign to a subject and affect their motivation, coordination, and overall mobility. Conducting gait studies in unconstrained natural settings instead, such as the subject’s Activities of Daily Life (ADL), could provide a more accurate assessment. To appropriately diagnose gait deficiencies, muscle activity should be recorded in parallel with typical kinematic studies. To achieve this, Electromyography (EMG) and kinematic are collected synchronously. Our protocol sMaSDP introduces a simplified markerless gait event detection pipeline for the segmentation of EMG signals via Inertial Measurement Unit (IMU) data, based on a publicly available dataset. This methodology intends to provide a simple, detailed sequence of processing steps for gait event detection via IMU and EMG, and serves as tutorial for beginners in unconstrained gait assessment studies. In an unconstrained gait experiment, 10 healthy subjects walk through a course designed to mimic everyday walking, with their kinematic and EMG data recorded, for a total of 20 trials. Five different walking modalities, such as level walking, ramp up/down, and staircase up/down are included. By segmenting and filtering the data, we generate an algorithm that detects heel-strike events, using a single IMU, and isolates EMG activity of gait cycles. Applicable to different datasets, sMaSDP was tested in healthy gait and gait data of Parkinson’s Disease (PD) patients. Using sMaSDP, we extracted muscle activity in healthy walking and identified heel-strike events in PD patient data. The algorithm parameters, such as expected velocity and cadence, are adjustable and can further improve the detection accuracy, and our emphasis on the wearable technologies makes this solution ideal for ADL gait studies.

Abstract
The UNEXMIN (Horizon 2020) and UNEXUP (EIT RawMaterials) projects developed a novel technology to send robots and even autonomously deliver optical images, 3D maps and other georeferenced scientific data from flooded underground environments, like abandoned mines, caves or wells. The concept turned into a market ready solution in seven years, where the last few years of field trials of the development beautifully demonstrating the technology's premier capabilities. Here in this paper, we focus on the wide variety of environments, circumstances and measurements where the UNEXMIN technology can be the best solution or the only solution to deliver certain research or engineering data. These are obtained from both simple and complex environments like different mines and caves, small and large cavities, long and tight tunnels and shafts, different visibility conditions, even different densities of the liquid medium where UX robots operated.

Abstract

Abstract
This paper introduces two machine learning optimization algorithms to significantly enhance position estimation in Reconfigurable Intelligent Surface (RIS) aided localization for mobile user equipment in Non-Line-of-Sight conditions. Leveraging the strengths of these algorithms, we present two methods capable of achieving extremely high accuracy, reaching sub-centimeter or even sub-millimeter levels at 3.5 GHz. The simulation results highlight the potential of these approaches, showing significant improvements in indoor mobile localization. The demonstrated precision and reliability of the proposed methods offer new opportunities for practical applications in real-world scenarios, particularly in Non-Line-of-Sight indoor localization. By evaluating four optimization techniques, we determine that a combination of a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) results in localization errors under 30 cm in 90 % of the cases, and under 5 mm for close to 85 % of cases when considering a simulated room of 10 m by 10m where two of the walls are equipped with RIS tiles.