Conveyor belts (CBs) are widely used for the continuous transport of bulk materials. CBs must be extremely reliable due to the cost associated with their failure in continuous production systems. Thus, it is highly relevant in terms of maintenance and planning to find solutions to reduce the existing stoppages from these assets. In this sense, it is essential to monitor and collect real-time data from this piece of equipment. This work presents a case study, where a model that combines the Lean Philosophy, Total Productive Maintenance (TPM), and the enabling technologies of Industry 4.0 is applied to a CB. The proposed model monitors the CB and provides data on its operation, which, using the calculation of indicators, allows a more accurate and thorough view and evaluation, contributing to improving and supporting decision making by those responsible for maintenance. The data collected by the sensor help those responsible for maintenance and production, in the readjustment of more accurate and optimized planning, programming, and execution, supporting decision making in these areas. During the field test of a two-hour monitoring period (10 a.m. to 12 p.m.), the model identified six stoppages, resulting in approximately 88.6% of operational time for the conveyor. The field test showed that this model can result in more accurate maintenance decision making than conventional approaches. This research also contributes to the advancement of electronics and industrial automation sectors by empowering companies to transform maintenance methodologies. The potential of this approach and its implications for enhanced productivity and overall performance are therefore highlighted.

The work presented in this paper regards the improvement of a housekeeping system for data acquisition of a suborbital vehicle (VIRIATO rocket or launcher). The specifications regarding the vehicle are presented and hardware is chosen accordingly, considering commercial off-the-shelf components. Mechanical and thermal simulations are performed regarding the designed system and a physical prototype is manufactured, assembled and programmed. Functional and field test results resorting to unmanned aerial vehicles, as well as the system’s integration within VIRIATO project’s mock-up vehicle, are presented. These tests demonstrate the viability of this system as an independent data acquisition system, and simulation results show that commercial off-the-shelf components have the capability of surviving expected launch environments.

The need for creative solutions in real-time health monitoring has been highlighted by the rise in health-related incidents involving drivers of motor vehicles. It has led to the development of wearable technology that seamlessly integrates with the Internet of Medical Things (IoMT) to improve driver safety and healthcare responsiveness. The development of a revolutionary wearable technology system is presented in this study as an innovative approach to vehicle safety and healthcare. This system's real-time ability to track a driver's health is a significant development in guaranteeing driver safety and wellness. The study examines the hardware component's complex design and implementation, particularly concerning the printed circuit board (PCB) layout and electrical schematic. The gadget emphasizes wearability, robustness, affordability, and user-friendliness and is a shining example of valuable and effective medical technology. The research delves deeper into possible improvements for the system, like adding complex algorithms and a user-friendly interface. Enhancing user involvement and system intelligence hopes to maximize the system's potential for real-time health monitoring. The significance of this study in utilizing Internet of Medical Things (IoMT) technology is highlighted by its junction with multiple fields, including electronics, hardware engineering, human-computer interaction, and health informatics. This dissertation emphasizes the potential of wearable technology in bridging the gap between healthcare monitoring and vehicle safety by focusing on real-time health monitoring in the automotive context

This review describes the multiple working principles and technological solutions that have been developed and applied in features extraction of horticultural products, both in the field and industrial environments. This paper intends to contribute to the development and utilization of portable agricultural equipments. Currently the research on sensing technology for fruits and vegetables is focused on computer vision equipment and algorithms, being the main purpose to support the automation of various agricultural and industrial processes, although there is a lack of integration of these technologies in useful systems.

The idea of monitoring several types of parameters in various environments has been motivating significant research works in Internet of Things (IoT). This paper presents the design and construction of iSensA, a system for integrating and collecting information from sensors. The solution implements a multi-sensor monitoring system and then expands the monitoring concept to an IoT solution, by employing multi-network access, Web services, database and web and mobile applications for user interaction. iSensA system is highly configurable, enabling several monitoring solutions with different types of sensors. Experiments have been performed on real application scenarios to validate and evaluate our proposition.

Using the AdaBoost method may increase the accuracy and reliability of a framework for daily activities and environment recognition. Mobile devices have several types of sensors, including motion, magnetic, and location sensors, that allow accurate identification of daily activities and environment. This paper focuses on the review of the studies that use the AdaBoost method with the sensors available in mobile devices. This research identified the research works written in English about the recognition of daily activities and environment recognition using the AdaBoost method with the data obtained from the sensors available in mobile devices that were published between 2012 and 2018. Thus, 13 studies were selected and analysed from 151 identified records in the searched databases. The results proved the reliability of the method for daily activities and environment recognition, highlighting the use of several features, including the mean, standard deviation, pitch, roll, azimuth, and median absolute deviation of the signal of motion sensors, and the mean of the signal of magnetic sensors. When reported, the analysed studies presented an accuracy higher than 80% in recognition of daily activities and environments with the Adaboost method.

This paper describes the experimental method focused on the acquisition of various physiological parameters during different effort levels of physical exercise like walking and running at several velocities. The study involved 57 young and adult people, 43 male and 14 female (24,37±5,96 years), from which 48 were soldiers belonging to the Infantry Regiment n.° 13 (RI13) of the Portuguese Army and 9 were teachers or college students of Sport Sciences, physically active but not competitive. The experimental measures provide a set of information that offers insight about the health status and physical performance of the subjects during exercise. This experimental method procedure is suited for the acquisition of physiological parameters with both the wireless physiological data acquisition systems such as the bioPlux and the respiratory analyzer gas systems such as Cosmed K4b2.The data was collected to allow the definition of a model that will be used to estimate the energy expenditure of a subject using a wireless physiological data acquisition system, which is much more comfortable and suitable to monitor physical exercise in everyday use than the standard method that makes use of a respiratory gas analysis system.

This paper presents the current status and future trends of sensor technologies for food products traceability, specifically for systems devoted to be applied in the cold storage and refrigerated transport of horticultural products. The available monitoring technologies such as dataloggers, TTI, WSN, RFID, and combined approaches are evaluated and compared in terms of dimensions, sensor parameter (temperature, relative humidity, water vapor, ethylene and other gases released during the biological processes of fruit), robustness, reliability, energy autonomy, bandwidth (if applicable), connection, among others. The aim of the study consists in evaluating how these technologies can be successfully applied for traceability of horticultural products and are what the future trends in terms of technical and technological specifications that will guide the development of these systems. It is expected that the increasing use of these systems for food traceability increases food quality and safety, reducing food waste.

Background: Off-the-shelf-mobile devices have several sensors available onboard that may be used for the recognition of Activities of Daily Living (ADL) and the environments where they are performed. This research is focused on the development of Ambient Assisted Living (AAL) systems, using mobile devices for the acquisition of the different types of data related to the physical and physiological conditions of the subjects and the environments. Mobile devices with the Android Operating Systems are the least expensive and exhibit the biggest market while providing a variety of models and onboard sensors. Objective: This paper describes the implementation considerations, challenges and solutions about a framework for the recognition of ADL and the environments, provided as an Android library. The framework is a function of the number of sensors available in different mobile devices and utilizes a variety of activity recognition algorithms to provide a rapid feedback to the user. Methods: The Android library includes data fusion, data processing, features engineering and classification methods. The sensors that may be used are the accelerometer, the gyroscope, the magnetometer, the Global Positioning System (GPS) receiver and the microphone. The data processing includes the application of data cleaning methods and the extraction of features, which are used with Deep Neural Networks (DNN) for the classification of ADL and environment. Throughout this work, the limitations of the mobile devices were explored and their effects have been minimized. Results: The implementation of the Android library reported an overall accuracy between 58.02% and 89.15%, depending on the number of sensors used and the number of ADL and environments recognized. Compared with the results available in the literature, the performance of the library reported a mean improvement of 2.93%, and they do not differ at the maximum found in prior work, that based on the Student’s t-test. Conclusion: This study proves that ADL like walking, going upstairs and downstairs, running, watching TV, driving, sleeping and standing activities, and the bedroom, cooking/kitchen, gym, classroom, hall, living room, bar, library and street environments may be recognized with the sensors available in off-the-shelf mobile devices. Finally, these results may act as a preliminary research for the development of a personal digital life coach with a multi-sensor mobile device commonly used daily.

This paper presents the development of an innovative control, regulation and command system for hydronic radiant floors, more flexible and efficient that guarantees a better thermal comfort to the user and simultaneously improves the energy efficiency of this type of heating system. The majority of the actual control of hydronic radiant floors is done by thermostats that measure the air temperature and control the actuators (pumps and valves) in order to maintain the room at the specified temperature. These systems requires the frequent adjustment of thermostats set-point in order to obtain thermal comfort as it depends on other factors than just the air temperature, such as, the air humidity, external environmental conditions, radiant temperature, among others. This paper presents a control, regulation and command solution that requires minimum user intervention, as the user only has to choose the desirable thermal comfort level. The control algorithm is based on the calculation of PMV (Predicted Mean Vote) index as defined on Thermal Comfort Standard ISO 7730. Another advantage of the proposed system is related to the wireless and energy harvesting sensors and actuators that provide much more flexibility to the system.