The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient's autonomy.

This study consists of a systematic literature review that aims to explore the potential of integrating wearable sensor data and machine learning (ML) techniques for predicting stress episodes. It aims to identify prevalent sensors, key physiological features, and the effectiveness of ML methods in real-world stress monitoring and prediction. This systematic review follows the PRISMA methodology, analyzing literature from January 2010 to June 2025. Data sources included IEEE Xplore, Elsevier, Springer, Multidisciplinary Digital Publishing Institute (MDPI), and paper repositories such as PubMed Central and the Association of Computing Machinery (ACM). The inclusion criteria encompassed studies that employed wearable devices for ML stress prediction, focusing on physiological data such as heart rate variability (HRV), skin conductance, and sleep patterns. Articles were screened for originality, clinical relevance, and methodological rigor. Key findings highlighted the use of diverse wearable sensors, including electrodermal activity (EDA), photoplethysmography (PPG), and accelerometers. Commonly extracted features included HRV metrics, skin conductance levels, and respiratory patterns. ML models, such as Random Forest (RF), Support Vector Machines (SVM), and deep neural networks (DNN), have demonstrated high predictive accuracy (e.g., up to 99%). Despite promising results, challenges such as small sample sizes, variability in data quality, and the need for standardized protocols were noted. Wearable sensors combined with ML algorithms provide scalable, real-time stress monitoring solutions, emphasizing proactive healthcare management. However, advancing this field requires addressing limitations through interdisciplinary collaboration and focusing on the accessibility and usability of technologies. This study highlights the transformative role of wearable technologies in predicting stress, with implications for personalized health interventions, mental health support, and enhanced healthcare efficiency.

Modern commercial poultry farming typically occurs indoors, where partial or complete environmental control is employed to enhance production efficiency. Maintaining optimal conditions, such as temperature, relative humidity, carbon dioxide, and ammonia levels, is essential for ensuring bird comfort and maximizing productivity. Monitoring the conditions of poultry houses requires reliable and intelligent management systems. This study introduces a Wireless Monitoring and Control System developed to regulate environmental conditions within poultry facilities. The system continuously monitors key parameters via a network of distributed sensor nodes, which transmit data wirelessly to a centralized control unit using Wi-Fi. The control unit processes the incoming data, stores it in a database, and adjusts actuators accordingly to maintain ideal conditions. A web-based dashboard allows users to monitor and control the environment in real time. Field testing confirmed the system’s effectiveness in keeping conditions optimal, supporting poultry welfare and operational efficiency.

This work evaluates the use of a programmable logic controller (PLC) from Phoenix Contact’s PLCnext ecosystem as an image processing platform. PLCnext controllers provide the functions of “classical” industrial controllers, but they are based on the Linux operating system, also allowing for the use of software tools usually associated with computers. Visual processing applications in the Python programming language using the OpenCV library are implemented in the PLC using this feature. This research is focused on evaluating the use of this PLC as an image processing platform, particularly for industrial machine vision applications. The methodology is based on comparing the PLC’s performance against a computer using standard image processing algorithms. In addition, a demonstration application based on a real-world scenario for quality control by visual inspection is presented. It is concluded that despite significant limitations in processing power, the simultaneous use of the PLC as an industrial controller and image processing platform is feasible for applications of low complexity and undemanding cycle times, providing valuable insights and benchmarks for the scientific community interested in the convergence of industrial automation and computer vision technologies.