Wearable textile electrodes for the detection of biopotentials are a promising tool for the monitoring and early diagnosis of chronic diseases. We present a comparative study of the electrical characteristics of four textile electrodes manufactured from common fabrics treated with a conductive polymer, a commercial fabric, and disposable Ag/AgCl electrodes. These characteristics will allow identifying the performance of the materials when used as ECG electrodes. The electrodes were subjected to different electrical tests, and complemented with conductivity calculations and microscopic images to determine their feasibility in the detection of ECG signals. We evaluated four electrical characteristics: contact impedance, electrode polarization, noise, and long-term performance. We analyzed PEDOT:PSS treated fabrics based on cotton, cotton–polyester, lycra and polyester; also a commercial fabric made of silver-plated nylon Shielde® Med-Tex P130, and commercial Ag/AgCl electrodes. We calculated conductivity from the surface resistance and, analyzed their surface at a microscopic level. Rwizard was used in the statistical analysis. The results showed that textile electrodes treated with PEDOT:PSS are suitable for the detection of ECG signals. The error detecting features of the ECG signal was lower than 2% and the electrodes kept working properly after 36 h of continuous use. Even though the contact impedance and the polarization level in textile electrodes were greater than in commercial electrodes, these parameters did not affect the acquisition of the ECG signals. Fabrics conductivity calculations were consistent to the contact impedance.

Computational power and interconnection capacities have contributed smartphones and tablets to become an essential tool to medical sector. Developed countries have demonstrated that mobile devices may be considered as equipment for tracking, monitoring, reading updating and treatment assistance; hence, they will be portable health care support machines. We have developed a mobile application for ambulatory electrocardiographic monitoring based on Android and iOS (iPhone Operating System), which contributes to proceed with accuracy in daily activities connecting different zones. The application, composed by buttons, switches, sliders and rotating screens, runs under two scenarios: patient and doctor mode, allowing medical personnel to define different configurations. Both modes permit users to visualize in real time captured readings and tracings. Its advantages have increased in capabilities and decreased complexities, becoming one of the main instruments at hospital environments. It has been proven that such devices have better performance through the usage of cloud computing services, low power consumption and low number of connections. This paper aims to stimulate medical personnel the use of capacities for mobile devices through medical applications and guide diagnose procedures with efficiency.

Medical applications and clinical environments set new demands on sensor network technologies, one of the key research areas in computer science and health telemonitoring. This paper presents a wireless systematic architecture, a hardware and software platform based on low-power integrated circuits, on-chip signal processing components and communications. The structure of such architecture consists of lightweight miniature nodes placed strategically on patients to allow non-invasive, continuous and ambulatory health monitoring and inexpensive, to provide medical readings on local devices and clinical records applications on web browsers and mobile devices. The main goal of this project is the development of an autonomous wireless monitoring system for vital signs with battery-powered nodes and medical sensors. It acquires, stores and transmits signals to determine the states of oxygenation, heart and respiratory rates, and temperature in real time to a central monitoring station, that integrates a touchscreen graphical interface and mini-OLED displays. This work provides self-monitored environments minimizing the requirements for primary needs, early medical attention, optimizing premature diagnosis and comfort of patients, through a set of algorithms that contribute to the performance of the nodes generating significant benefits on human lifetime through early disease detection.

The aim of this project is to present the development of a lower limb rehabilitation system, which uses active joint exoskeleton, a Functional Electrical Stimulation (FES) device, control algorithms and an electromyography surface module.

Wildfires pose significant threats to tropical ecosystems, yet, fire precursors in these regions are not well understood. This study conducts a statistical analysis of fire precursors in the tropical area of Antioquia, Colombia, using remote sensing data from VIIRS and MODIS satellites. We integrated and pre-processed data on fire occurrences, the Enhanced Vegetation Index (EVI), land classifications, and various weather and soil variables to ensure compatibility for statistical analysis. Our findings reveal that reductions in EVI by 3.9% and relative humidity by 21%, coupled with increases in air temperature by 4.9 °C, soil temperature by 2.77 °C, and wind speed by 0.17 m/s, are significant precursors to fire occurrences in this tropical region. Notably, precipitation and soil moisture did not show definitive correlations with fire events, contrasting with findings in temperate regions. Spatial and temporal analyses indicate that fires are more frequent during the first three months of the year and tend to recur in specific areas, suggesting persistent environmental risks and human influences, particularly in built-up areas. The limitations of the study include reliance on a single land classification dataset from 2020, which may not account for land cover changes over the study period, and the coarse temporal resolution of EVI data that could overlook rapid vegetation changes. Despite these limitations, the results provide valuable insights for enhancing early warning systems, informing policy development, and improving resource allocation for fire risk management in tropical ecosystems.

For many years, social participation has been pointed out as the main component of smart cities and a key to regulating society in terms of mobility. In this work, we present a social metric for the level of law compliance from drivers regarding pedestrians, at permitted crossings, the Index of Respect to Pedestrians Priority (IRPP), and propose a Pedestrian Report Management System (PeReMaS), based on a mobile app, to increase IRPP value over time. The system is tested in a university campus. A sociocybernetic analysis was performed, to assess the potential of the PeReMaS. Results have shown that social progress, concerning the level of traffic law compliance towards pedestrians, could be measured in terms of IRPP. Also, the PeReMaS empowers pedestrians, and this seems to influence positively the behavior of car drivers. These findings suggest this is an approach worth considering in the evolution of pedestrian smart cities.