Cardiovascular diseases (CVDs) are the leading cause of death globally. An effective strategy to mitigate the burden of CVDs has been to monitor patients’ biomedical variables during daily activities with wearable technology. Nowadays, technological advance has contributed to wearables technology by reducing the size of the devices, improving the accuracy of sensing biomedical variables to be devices with relatively low energy consumption that can manage security and privacy of the patient’s medical information, have adaptability to any data storage system, and have reasonable costs with regard to the traditional scheme where the patient must go to a hospital for an electrocardiogram, thus contributing a serious option in diagnosis and treatment of CVDs. In this work, we review commercial and noncommercial wearable devices used to monitor CVD biomedical variables. Our main findings revealed that commercial wearables usually include smart wristbands, patches, and smartwatches, and they generally monitor variables such as heart rate, blood oxygen saturation, and electrocardiogram data. Noncommercial wearables focus on monitoring electrocardiogram and photoplethysmography data, and they mostly include accelerometers and smartwatches for detecting atrial fibrillation and heart failure. However, using wearable devices without healthy personal habits will cause disappointing results in the patient’s health.

Neurodegenerative diseases (NDDs) are among the 10 causes of death worldwide. The effects of NDDs, including irreversible motor impairments, have an impact not only on patients themselves but also on their families and social environments. One strategy to mitigate the pain of NDDs is to early identify and remotely monitor related motor impairments using wearable devices. Technological progress has contributed to reducing the hardware complexity of mobile devices while simultaneously improving their efficiency in terms of data collection and processing and energy consumption. However, perhaps the greatest challenges of current mobile devices are to successfully manage the security and privacy of patient medical data and maintain reasonable costs with respect to the traditional patient consultation scheme. In this work, we conclude: (1) Falls are most monitored for Parkinson’s disease, while tremors predominate in epilepsy and Alzheimer’s disease. These findings will provide guidance for wearable device manufacturers to strengthen areas of opportunity that need to be addressed, and (2) Of the total universe of commercial wearables devices that are available on the market, only a few have FDA approval, which means that there is a large number of devices that do not safeguard the integrity of the users who use them.

In this work, the evaluation of a spectrum sensing strategy based on the Frequency Domain Entropy applied to cognitive radio networks is presented. Entropy estimation is performed using Bartlett periodogram. A tradeoff between variance and the spectral resolution for Bartlett periodogram is figured out. This tradeoff affects the probability of detection and false alarm of the spectrum sensing strategy under environments with low signal-to-noise ratio and noise uncertainty. The Entropy detector is optimal when the product of the number of segments and the number of points used is equal to the number of available samples of the received signal.

To support the understanding of cardiovascular monitoring and physiological signal processing, we present a portable, open-source photoplethysmography (PPG) acquisition platform developed for educational and research applications. The system is built entirely with commercial off-the-shelf components and centers around an ESP32 microcontroller, which performs high-speed analog signal acquisition at 500 samples per second, alongside real-time control, and wireless communication. A cross-platform, Python-based graphical user interface enables real-time signal visualization, peak detection, and the computation of heart rate variability (HRV) metrics, including RMSSD and SDNN, during offline analysis. All hardware and software resources are openly available to enable replication and further development. This project emphasizes accessibility, transparency, and hands-on learning in biomedical signal acquisition. System functionality is validated offline through controlled data collection from human subjects, demonstrating results consistent with established HRV benchmarks.

Liver diseases are a widespread and severe health concern, affecting millions worldwide. Non-alcoholic fatty liver disease (NAFLD) alone affects one-third of the global population, with some Latin American countries seeing rates exceeding 50%. This alarming trend has prompted researchers to explore new methods for identifying those at risk. One promising approach is using Machine Learning Algorithms (MLAs), which can help predict critical factors contributing to liver disease development. Our study examined nine different MLAs across four datasets to determine their effectiveness in predicting this condition. We analyzed each algorithm’s performance using five important metrics: accuracy, precision, recall, f1-score, and roc_auc. Our results showed that these algorithms were highly effective when used individually and as part of an ensemble modeling technique such as bagging or boosting. We identified alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and albumin as the top four attributes most strongly associated with non-alcoholic fatty liver disease risk across all datasets. Gamma-glutamyl transpeptidase (GGT), hemoglobin, age, and prothrombin time also played significant roles. In conclusion, this research provides valuable insights into how we can better detect and prevent non-alcoholic fatty liver diseases by leveraging advanced machine learning techniques. As such, it represents an exciting opportunity for healthcare professionals seeking more accurate diagnostic tools while improving patient outcomes globally.

This work investigates the stability of a linear oscillator with a periodically varying stiffness composed of constant and linearly time-dependent segments. By combining Floquet theory with an analytical formulation in terms of Airy functions, the monodromy matrix is obtained in closed form and the characteristic multipliers that determine the stability regime are calculated. Unlike the classical literature on Hill or Mathieu systems, where the stiffness profile is assumed to switch instantaneously or vary sinusoidally, the present model explicitly incorporates finite transition times through linear ramps. This allows us to quantify how the duration of these transitions affects the onset of parametric resonance. The resulting stability map reveals alternating bands of stable and unstable regions reminiscent of Arnold tongues, and shows that the proportion of the cycle spent in the linear-ramp stage plays a decisive role in either promoting or suppressing instability. Overall, the study provides a compact analytical and numerical framework for assessing stability in periodically driven parametric systems of practical relevance in physics and engineering.