Industry 4.0 (I4.0) is built upon the capabilities of Internet of Things technologies that facilitate the recollection and processing of data. Originally conceived to improve the performance of manufacturing facilities, the field of application for I4.0 has expanded to reach most industrial sectors. To make the best use of the capabilities of I4.0, machine architectures and design paradigms have had to evolve. This is particularly important as the development of certain advanced manufacturing technologies has been passed from large companies to their subsidiaries and suppliers from around the world. This work discusses how design methodologies, such as those based on functional analysis, can incorporate new functions to enhance the architecture of machines. In particular, the article discusses how connectivity facilitates the development of smart manufacturing capabilities through the incorporation of I4.0 principles and resources that in turn improve the computing capacity available to machine controls and edge devices. These concepts are applied to the development of an in-line metrology station for automotive components. The impact on the design of the machine, particularly on the conception of the control, is analyzed. The resulting machine architecture allows for measurement of critical features of all parts as they are processed at the manufacturing floor, a critical operation in smart factories. Finally, this article discusses how the I4.0 infrastructure can be used to collect and process data to obtain useful information about the process.

The elderly population is constantly increasing, facing physical and cognitive limitations that require attention and care. Consequently, caregivers of older adults must maintain constant attention to their physical well-being. Falls are common and can produce serious consequences. The constant monitoring of vital signs is crucial to monitor health status and respond quickly to emergencies. Therefore, the design of a device that accurately detects risk situations in an older adult is a challenge. This paper presents the development of a bracelet-type device with Internet of Things (IoT) architecture for automatic fall detection and vital signs monitoring. Fall detection is performed by a machine learning algorithm trained on data obtained from an accelerometer. The Edge Impulse platform was used where the data processing and the learning block are configured. The trained model was implemented in an ESP32 microcontroller that detects falls with an accuracy of 91%. The device incorporates sensors for monitoring temperature, heart rate, and oxygen saturation that are measured on the wrist of the elderly. A mobile application for the caregiver of the elderly was implemented that issues alerts if the system detects falls, notifies vital sign values if these are outside the normal range, and presents the results in real time.

Los sistemas tradicionales de producción y manufactura se basan en una producción en masa o en lotes donde los productos poseen las mismas características para todos los clientes. Ante los cambios en las tendencias tecnologías y requerimientos de los clientes, surge la producción personalizada, en la cual el cliente es una fuente de información para la línea de fabricación inteligente. En el presente trabajo, se diseña un dispensador inteligente de frutos secos con el cual los clientes puedan realizar el pedido seleccionando la cantidad en gramos de cuatro tipos de frutos secos. En el sistema se implementa una arquitectura de Internet de las Cosas (IoT) compuesta de tres capas: dispositivos, procesamiento y servicios en la nube. La capa de dispositivos utiliza sensores, actuadores, y mecanismos para la dispensación. La capa de procesamiento de datos en la niebla se realiza mediante las tarjetas electrónicas Arduino Mega y ESP8266. En la capa de servicios en la nube, se desarrolló las interfaces Web para clientes y administradores, estas interactúan permanentemente con una base de datos MySQL. En las pruebas se obtuvo un tiempo máximo de retardo de actualización de datos de 1,3 segundos, el tiempo promedio de llegada de un nuevo pedido fue de 1,7 segundos y un error promedio de 0.8 gramos en la dispensación. El dispensador posee características como flexibilidad al preparar el producto, conectividad, interacción constante con los usuarios, y actualizaciones de estado del sistema en tiempo real que lo integran a las tecnologías usadas en la Industria 4.0.

In hospitals, IoT has facilitated connectivity between patients and medical services using historical health data. However, its adoption in hospital catering services has been slower. This work describes the implementation of an IoT system with a three-layer architecture: the first layer collects data on patient diets and environmental conditions from the food warehouse, the second layer processes this information, establishing rules and converting raw data into valuable information, and the third layer stores the data in the cloud, presenting it in a web application. A functional system was obtained that meets the needs of catering service personnel and the hospital in which it was implemented.