Auditoriums and theaters are buildings in which concerts, shows and conferences are held, offering a diverse and dynamic cultural offer to citizens. But people with impairment usually have difficulties in order to fully experience all those provided cultural activities, since such environments are not totally adapted to their necessities. For example, in an auditorium, visually impaired users have to be accompanied to their seats by event staff, as well as if the person wants to leave in the middle of the show (e.g. to the toilette) or even if they want to move around during breaks. The presented work, based on the deployment of Wireless Sensor Networks and Wireless Body Area Networks connected to an augmented reality device (HoloLens glasses) tries to aid in the autonomy of disabled people within this kind of environments, as well as in enhancing their show experience. For that purpose, intensive measurements have been taken in a real scenario (Baluarte Congress Center and Auditorium of Navarre) located in the city of Pamplona. The results show that this kind of environments present high wireless interference at different frequency bands due to the existing wireless systems deployed within them, such as multiple WiFi access points, wireless microphones or wireless communication systems used by the show staff. Therefore, radio channel simulations have been also performed with the aim of assessing the potential deployment of the proposed solution. The presented work can lead to the deployment of augmented reality systems within auditoriums and theaters, boosting the development of new applications.

This paper presents a new methodology for the estimation of mass and size of high-export-volume Vietnamese fruits (such as cavendish-type bananas and cucumbers), characterized by only its top-view images using image analysis techniques. Most previous works focused on volume estimation using a plurality of cameras in order to infer the three-dimensional information. In this work, we only use a single camera mounted on top of the fruit. A machine learning technique is also used to find the most essential features. We have found that our proposal lead to a relatively small estimation error compared to the results obtained from the measurements using a water-replacement method and a static digital scale. The results indicate that our system shows a great potential to be used in a real industrial setting. Future work will aim to investigate other features such as ripeness and bruises to increase the effectiveness and practicality of the system.

The purpose of the present study is to monitor tool condition in the grinding operation through the electromechanical impedance (EMI) by using wavelet analysis. To achieve this, a dressing experiment was conducted on an industrial aluminum oxide grinding wheel by fixing a stationary single-point diamond tool. The proposed approach was verified experimentally at various dressing tool conditions. The signals obtained from an EMI data acquisition system, composed of a piezoelectric diaphragm transducer attached to the tool holder, were processed by using the discrete wavelet transform. The approximation and detail coefficients obtained from wavelet decomposition were used to estimate the tool condition by using the correlation coefficient deviation metric (CCDM). The results show good performance in tool condition monitoring by the proposed technique, which effectively contributes to modern machine tool automation

Alcohol addiction is the third leading lifestyle-related cause of death in the United States. There are not enough support tools for alcoholics who want to quit alcohol consumption. The detection of drinking in a free-living environment may improve the just-in-time adaptive intervention to this behavior. Traditional methods to detect alcohol consumption suffer from long response time that hinders prompt intervention and prevention. This paper proposes to employ inertial sensors to automatically detect the drinking of alcohol in a natural environment by leveraging the hand gesture characteristics that are specific to drinking. Due to the lack of publicly available sensor dataset of alcohol drinking, this paper focused on the detection of general beverage drinking by exploiting the hand gestures. A public dataset containing seven daily activities (including hand-related activities such as eating, drinking, smoking, etc.) collected from 11 subjects in a controlled environment was adopted for this analysis. The detection model was developed using deep neural networks containing both convolutional and recurrent neural networks. The proposed approach achieved an F1-score accuracy of 0.87 in the Leave-One-Subject-Out (LOSO) cross-validation. We argue that the contributions of this paper would be useful when an alcohol-specific dataset becomes available.

This work gathers and presents three lectin-based impedimetric biosensors for the selective detection of specific aberrant cancer-associated O-glycans, namely STn, Tn and T antigens. These truncated glycans are well-established pan-carcinoma biomarkers that are synthesized by tumour cells during protein glycosylation. Glycoproteins carrying these aberrant glycans are then secreted into the blood stream, where they can be detected as cancer biomarkers.

Detection of aberrant O-glycoproteins in serum can be successfully performed by using lectin biosensors, as lectins show high selectivity towards particular glycan structures. Lectins are immobilized on the sensor surface, maintaining intact their binding ability towards the glycans present in the sample. For these three biosensors, Sambucus nigra agglutinin, Vicia villosa agglutinin and Arachis hypogeae agglutinin were used as biorecognition elements, with specificity for STn, Tn and T antigens, respectively. The binding event between each lectin and the corresponding aberrant O-glycan was monitored by electrochemical impedance spectroscopy, measuring the increase in the biosensor’s impedance after incubating the samples. The increase in impedance was related to the lectin-glycan complex formation.

The performance of the developed biosensors, prepared on screen printed gold electrodes, was evaluated, namely in what concerns selectivity, sensitivity, limit of detection, reproducibility and robustness. Furthermore, a thorough validation was carried out by analyzing serum samples from cancer patients and from healthy donors. Results showed that the three biosensors could efficiently discriminate between controls and patients. Moreover, by analyzing the same samples with the different biosensors, distinct glycosylation profiles could be observed.

The respiratory rate can be conveniently monitored by measuring the concentration of water vapor in the exhaled air. Since human breathing is a quite fast process, fast-responding sensors are required for this application. In addition, high-resolution breathing patterns could provide reliable information on the performance of the respiratory system, useful in medicine (spirometry, general anaesthesia, etc.) and sport fields. Although a variety of humidity sensors are described in the literature, those with a fast response form only a small subset. The use of zeolites as mechanically/thermally stable and inexpensive water sensors is well known, however these sensors have also fast response and high sensitivity, and therefore could result as adequate for respiratory rate monitoring. Natural clinoptilolite has been selected for this study because it has a high Si/Al ratio (5.3) which allows the material to experience significant conductivity changes by exposure to humidity. The basic mechanism of humidity detection in zeolites is based on the change of ionic conduction with the humidity level. In particular, owing to the presence of extra-framework cations, electrical transport at the surface of clinoptilolite depends on the environmental humidity. In fact, at room temperature, the cation mobility is very low in a dry atmosphere, because of the strong electrostatic interactions with framework negative charge, while it becomes much higher after that a water molecule has joined the cationic site. Thus, water adsorption modifies the charge carrier density, which reflects in a change of the intensity of current moving at the sample surface. Here, the temporal-evolution of sinusoidal current signal (5kHz), moving in a sample of natural clinoptilolite during the water adsorption-desorption process, has been recorded and analyzed to investigate the involved kinetic mechanism.

In recent years, there is an important trend in the organization of cross-country races and popular races where hundred people usually participate. In these events, runners usually subject the body to extreme situations that can lead to various types of indisposition and they can also suffer falls. Currently the electronic systems used in this type of racing refer only to whether a runner has passed through a checkpoint. However, it is necessary to implement systems that allow controlling the population of runners knowing their status all the times. For this reason, this paper proposes the design of a low cost system for monitoring and controlling runners in this type of event. The system is formed by a network architecture in infrastructure mode based on Low-Power Wide-Area Network (LPWAN) technology. Each runner will carry an electronic device that will give their position and vital signs to be monitored. Likewise, it will incorporate an S.O.S. button that will allow sending a warning to the organization in order to help the person. All these data will be sent through the network to a database which will allow the organization and the public attending the race to check where the runner is and the history of their vital signs. This paper shows the proposal of a design to our system. Therefore, the paper will show the different practical experiments we have been carried out with the devices that have allowed proposing this design.

The advancement of microelectronics industry in recent years has allowed a major expansion in the development of sensor-based equipment and applications, driven primarily by the cost reduction of MEMS devices. Currently, using this type of component, it is feasible to develop cost-effective systems aimed at early detection of failures in electrical machines and, in special cases, in three-phase induction motors (TIM). These devices, coupled with predictive maintenance records, can prevent unexpected shutdowns due to malfunctions and signal the need for actions to extend the life cycle of the equipment. This is a relevant topic, considering that the industrial sector is increasingly seeking for solutions based on non-destructive techniques (NDT) for preventive and predictive fault diagnosis. In this scenario, the objective of this work is to evaluate the application of a low-cost MEMS accelerometer to identify insulation failures in stator windings through vibration analysis. For this purpose, two MEMS accelerometers were coupled on either side of the frame of a TIM. Then, the vibration signals were acquired for different types and levels of insulation failures. The data thus obtained were processed using different metrics such as RMS, Kurtosis and Skewness. The results allowed to identify the insulation faults applied to the TIM, confirming the feasibility of applying the low-cost MEMS accelerometer in the vibration analysis for fault diagnosis.

The paper is focused on analysis of negative influence of the generated vibration and acoustic noise on a physiological and psychological state of an examined person lying in the scanning area of the magnetic resonance imaging (MRI) tomograph working with a low magnetic field. This negative influence on the human body and psychic can be monitored by measuring the blood pressure (BP), the heart rate (HR) and the speech signal recorded during MR scanning, as the stress is manifested by changes in the bloodstream and in the voice. The first part of the presented work is aimed at finding a methodology for measurement of different signals of a tested person lying in the scanning area of the MRI device. In the main investigation, the BP and HR parameters were measured in parallel for a person scanned during execution of the whole MR scan sequence. For this purpose, we apply the photo-plethysmography (PPG) using an optical sensor for non-invasive acquisition of vital information about the cardiovascular system from the skin surface. This optical sensor complies with the requirements for working in the magnetic field environment with present radio-frequency and electromagnetic disturbance. Variations in the photo-detector signal are related to changes in the blood volume inside the tissue. Signal filtering and further processing are necessary to obtain a clean PPG waveform that is then used to determine the instantaneous heart rate. In the frame of our experiments, different types of portable BP and HR measuring devices were tested and compared.

The farm management practices done by machinery generates a high acoustical impact. The possible acoustic variations in terms of equivalent level ($L_{eq}$) and type of noise can cause stress to the animals, which can affect the well-being of broilers reducing the food and water ingest. In this work, we conduct a preliminary analysis of the acoustical impact generated by the farm management in a intensive broiler poultry farm of 25.000 birds. The raw sound is recorded during the first two weeks of the birds life, focusing the study on the first week. To create the dataset we randomly select some files from each day of the study and they are analysed and labelled manually using an audio analysis software. The acoustical events studied are fan, food, water supply and background noise with broiler singing, based on duration, impact and Signal to Noise Ratio (SNR). The analysis conculdes that the main acoustical pollution source in a broilers' farm is fan, and that it has an elevate acoustical impact. Nevertheless, the most frequent acoustical noise source active is food supply, but with less $L_{eq}$ impact.