Relevant to mobile health, the design of a portable electrocardiograph (ECG) device using AD823X microchips as the analog front-end is presented. Starting with the evaluation board of the chip, open-source hardware and software components were integrated into a breadboard prototype. This required modifying the microchip with the breadboard-friendly Arduino Nano board in addition to a data logger and a Bluetooth breakout board. The digitized ECG signal can be transmitted by serial cable, via Bluetooth to a PC, or to an Android smartphone system for visualization. The data logging shield provides gigabytes of storage, as the signal is recorded to a microSD card adapter. A menu incorporates the device’s several operating modes. Simulation and testing assessed the system stability and performance parameters in terms of not losing any sample data throughout the length of the recording and finding the maximum sampling frequency; and validation determined and resolved problems that arose in open-source development. Ultimately, a custom printed circuit board was produced requiring advanced manufacturing options of 2.5 mils trace widths for the small package components. The fabricated device did not degrade the AD823X noise performance, and an ECG waveform with negligible distortion was obtained. The maximum number of samples/second was 2380 Hz in serial cable transmission, whereas in microSD recording mode, a continuous ECG signal for up to 36 h at 500 Hz was verified. A low-cost, high-quality portable ECG for long-term monitoring prototype that reasonably complies with electrical safety regulations and medical equipment design was realized.

Interoperability is defined as the ability of a system or device to communicate between different technologies and software applications. This allows the exchange and use of data in an efficient, precise, and robust way. The present article gives researchers and healthcare information systems developers a qualitative and quantitative synthesis of the state of knowledge related to data formats and data standards proposed for mHealth devices interoperability in healthcare information systems that retrieve and store ECG data. We carry out a scoping review to answer to following questions: (1) What digital data formats or data standards have been proposed for the interoperability of electrocardiograph data between traditional healthcare information systems and mobile healthcare information systems? (2) What are the advantages and disadvantages of these data formats or data standards? The scoping review was conducted in four databases in accordance with the JBI methodology for scoping reviews, and in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). A total of 4018 studies were identified of which 30 studies met the inclusion criteria. Based on our findings, we identify four standards and nine formats for capturing and storing streaming ECG data in mobile health applications. The standards used were HL7, SCP-ECG, x73-PHD, and PDF/A. Formats include CSV, PDF-ECG, and seven XML-based formats. These are ECG-XML, HL7-XML, mPCG-XML, mECGML, JSON, SaECG, and CDA R2.

This paper presents the design of a portable electrocardiograph (ECG) device using the AD8232 microchip as the analog front-end (AFE). Starting with the manufacturer’s evaluation board of the AFE chip for testing circuit configurations, open-source hardware and software components were integrated into a breadboard prototype. Ultimately, a custom printed circuit board (PCB) was produced. The prototype required to accommodate the microchip on a SMD-to-DIP adapter for testing with the breadboard-friendly Arduino microcontroller alongside a data logger and a Bluetooth breakout board. The analog ECG signal from the AFE output was digitized using one channel of the 10-bit analog-to-digital Converter (ADC) of the ATmega328 microcontroller contained in the Arduino Nano board. The digitized ECG signal can be transmitted not only by serial cable using the Arduino functions, but also via Bluetooth to a PC or to an Android smartphone system when the HC-06 shield is used. The data logging shield provides gigabytes of storage, and the signal is recorded to a micro SD card adapter along with the date and time stamp data of the sample capture (real-time clock provided). In addition to hardware and software development, a simulation was used in the analog circuit design with SPICE Multisim software and the related macromodel library to assess system stability. Besides the analog filters in the AFE stage, digital filtering by means of simple difference equations was investigated. A menu was incorporated to choose from the several modes of operation of the device. The ECG test signals were obtained from a patient simulator (SimCube) and real patients. A portable ECG system for monitoring applications that complies with electrical safety regulations and medical equipment design was realized.

The growing demand for renewable energy positions it as a cornerstone for climate change mitigation and greenhouse gas emissions reduction. Although renewable energy sources generate around 30% of global electricity, their production and deployment involve significant environmental challenges. This review analyzes renewable energy projects from a life cycle perspective, focusing on environmental impacts throughout the supply chain. Particular emphasis is placed on the energy-intensive nature of manufacturing phases, which account for 60% to 80% of total emissions. The extraction of critical raw materials such as neodymium, dysprosium, indium, tellurium, and silicon is associated with emission levels ranging from 0.02 to 0.09 kg of carbon dioxide equivalent per kilowatt-hour for rare earth elements, along with an estimated average land degradation of 0.2 hectares per megawatt installed. Furthermore, the production of solar-grade silicon for photovoltaic panels consumes approximately 293 kilowatt-hours of electricity per kilogram, significantly contributing to the overall environmental footprint. Through a comprehensive review of the existing literature, this study integrates life cycle assessment and sustainable supply chain management approaches to identify environmental hotspots, quantify emissions, and propose strategic improvements. The analysis provides a structured, systematized, and data-driven evaluation, highlighting the relevance of circular economy principles, advanced recycling technologies, and digital innovations to enhance sustainability, traceability, and resilience in renewable energy supply chains. This work offers actionable insights for decision-makers and policymakers to guide the low-carbon transition.

Electromyographic signals have been used with low-degree-of-freedom prostheses, and recently with multifunctional prostheses. Currently, they are also being used as inputs in the human–computer interface that controls interaction through hand gestures. Although there is a gap between academic publications on the control of an upper-limb prosthesis developed in laboratories and its service in the natural environment, there are attempts to achieve easier control using multiple muscle signals. This work contributes to this, using a database and biomechanical simulation software, both open access, to seek simplicity in the classifiers, anticipating their implementation in microcontrollers and their execution in real time. Fifteen predefined finger movements of the hand were identified using classic classifiers such as Bayes, linear and quadratic discriminant analysis. The idealized movements of the database were modeled with Opensim for visualization. Combinations of two preprocessing methods—the forward sequential selection method and the feature normalization method—were evaluated to increase the efficiency of these classifiers. The statistical methods of cross-validation, analysis of variance (ANOVA) and Duncan were used to validate the results. Furthermore, the classifier with the best recognition result was redesigned into a new feature space using the sparse matrix algorithm to improve it, and to determine which features can be eliminated without degrading the classification. The classifiers yielded promising results—the quadratic discriminant being the best, achieving an average recognition rate for each individual considered of 96.16%, and with 78.36% for the total sample group of the eight subjects, in an independent test dataset. The study ends with the visual analysis under Opensim of the classified movements, in which the usefulness of this simulation tool is appreciated by revealing the muscular participation, which can be useful during the design of a multifunctional prosthesis.

Autonomous electronic systems are becoming increasingly important in people’s lives, as a result of advances in efficient energy storage systems, devices that can be permanently implanted in humans, and the trend towards compact devices that function as an extension of the human body. In addition, strategies continue to be found for the integration of energy harvesting in a constant and efficient manner. Covering the numerous advances made in biomedical devices can be quite overwhelming. This work presents a review of the latest strategies developed to produce energy from human body activity, the energy collectors for devices, and the strategies to create ultra-low-power wearable devices. The review focuses on the potential energy sufficiency required to power devices that can be implanted or worn, while also providing information about the patient’s condition. A comparison between the energies produced by different instruments and the improvements in the development of low-consumption devices is presented, with a focus on the type of medical devices. A new approach is established to classify and locate the most recent advances in autonomous systems in biomedicine based on their complexity/function.