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Nowadays, in the most developed countries, modern society is moving towards scenarios in which the self-sufficiency elderly people live alone in their homes. An automatic remote monitoring system using wearable sensors is becoming even more important in Ambient Assisted Living applications. For this type of services, it is important that IoT sensors networks, which are generally composed of devices with limited computing power and storage, implement reliable communication among sensors and the Internet. There are several specialized protocols for the Internet of Things proposed by the scientific community, each characterized by its own levels of Quality of Services. The emergence of new protocols forces the need for developing proxying systems able to intermediate among different types of networks and to translate the relative protocols. In this paper, we propose a complete architecture for monitoring and managing wearable devices, and, in particular, fall detection ones. Our system uses a cross protocol proxy and a device with CoAP and MQTT as application level protocols, while it exploits the NB-IoT at physical and data-link levels. The goal of this work is the performance evaluation of the proposed solution in terms of Throughput, Round Trip Time and Delay. The results highlight the low latency reached by the proposed system architecture thanks to the implemented protocols.

Industrial context of today, driven by the Industry 4.0 paradigm, is overwhelmed by data. Decreasing cost of innovative technologies, and recent market dynamics have pushed and pulled respectively for those architectures and practices in which data are the masters. While advancing, we have to take care of waste, even though intangibility of data makes them hardly connected to waste. In this paper we are going to reflect on data intensive context of today, focusing on the industrial sector. A smart approach for fully exploiting data collecting infrastructures is proposed, and its declination in a prognostic and health management (PHM) use case set inside an automatic painting system is presented. The contributions of this papers are mainly two: first of all, the general conceptual take-away of "data re-use" is presented and discussed. Moreover, a PHM solution for painting system's number plates, based on optical character recognition (OCR), is proposed and tested as a proof-of-concept for the "data re-use" concept. Summarizing, the already-in-use data sharing principle for achieving transparency and integration inside Industry 4.0, is presented as complementary with the proposed "data re-use", in order to develop a really sustainable shift toward the future.

Type 1 diabetes is a disease affecting beta cells of the pancreas and it’s responsible for a decreased insulin secretion, leading to an increased blood glucose level. The traditional method for glucose treatment is based on finger-stick measurement of the blood glucose concentration and consequent manual insulin injection. Nowadays insulin pumps and continuous glucose monitoring systems are replacing them, being simpler and automatized. This paper focuses on analyzing and improving the knowledge about which Machine Learning algorithms can work best with glycaemic data and tries to find out the relation between insulin pump settings and glycaemic control. The dataset is composed of 90 days of recordings taken from 16 children and adolescents. Three Machine Learning approaches, two for classification, Logistic Regression (LR) and Random Forest (RL), and one for regression, Multivariate Linear Regression (MLR), have been used for the purpose. Specifically, the pump settings analysis was performed based on the Time In Range (TIR) computation and comparison consequent to pump setting changes. RF and MLR have shown the best results, while, for the settings’ analysis, the data show a discrete correlation between changes and TIRs. This study provides an interesting closer look at the data recorded by the insulin pump and a suitable starting point for a thorough and complete analysis of them.

The rapid ageing of society makes necessary the development of advanced technologies for the identification of frailty. In this paper, we present a system for mobility evaluation in frailty phenotype assessment. The system is equipped with wireless, small and non-invasive wearable sensors for an objective evaluation of mobility. The paper proposes an optimization of gait analysis algorithm using a dynamic threshold. The results obtained from a comparison with the gold standard show errors of 3.7% for double support, 5.1% for stride length, and 5.8% for stride speed. Moreover, a simple and automatic tool, which estimates postural and walking parameters to assist medical staff in assessing frailty, is developed.

Bluetooth mesh is a recent technology built on the Bluetooth Low Energy protocol stack architecture, focusing on the Internet of Things. It represents an excellent solution for commercial and industrial lighting applications, though it is still evolving. One of the biggest challenges of the Bluetooth mesh network is the improvement of confirmation messages reception. In a Bluetooth mesh network, determining the Status of the received messages is a critical aspect that can generate unexpected issues when multiple devices respond simultaneously, as it may occur in some lighting applications. This behavior can reduce the probability of message delivery due to collisions, especially when the number of devices in the network increases. This paper aims to improve the reliability in receiving confirmation messages in a Bluetooth mesh network by proposing a new technique of spreading Status overtime. To evaluate the proposed technique's performance, we compare our technique with a Bluetooth mesh network with standard configuration using real nodes experimental setup. We evaluated our results in terms of packet-loss rate, obtaining 98.84% of the Status received for the network with our optimized configuration and 96.98% for those with the standard configuration. Finally, an in-depth performance evaluation method for the analysis of the lost Status was also conducted.

The main objective of this study is the realization of advanced methods of automatic evaluation and continuous monitoring tools for the support to the medical evaluation of Parkinson’s disease (PD) and to the follow up of the patient also through the presentation and classification of data through a universally recognized clinical scale. We have developed an embedded wearable device integrating a tri-axial accelerometer, a tri-axial gyroscope, and a tri-axial magnetometer, realizing a sensor with embedded data fusion and aggregation algorithms. Two different classification and detection algorithms are implemented to provide a comprehensive description and objective quantification of the two most investigated PD’s symptoms as tremor and freezing of gait (FoG). The system is usable in clinical and diagnostic settings showing its effectiveness above all in patients home monitoring through the upload in Cloud and the real-time processing of the acquired data. In the latter case, it allows the detection and objectification of PD symptoms manifested by the patient in everyday life, thereby providing the doctor with essential information for the dosage and effective personalization of drug therapy. For the system validation, a series of tests were carried out according to a defined experimental protocol on a sample of PD patients and healthy subjects with the presence of a video recording system and under the supervision of a neurologists team. The results obtained show a system’s accuracy of 97.7% for tremor classification and 99.7% for FoG events detection.

The rapid worldwide aging makes necessary the development of advanced technologies for the objective identification of diseases and disabilities onset. Accordingly, we need to regard the biological age as an alternative and more reliable indicator of the physiological decline of individuals, respect to the simple chronological age. In this paper, we present an eHealth system for estimate the biological age of elderly people starting from the assessment of the frailty phenotype. The frailty phenotype evaluation is made possible using a standard protocol for data acquisition and a cloud application for processing and storing data. The proposed eHealth system is also equipped with wireless, small and non-invasive wearable sensors for an objective evaluation of the mobility of a subject. The eHealth system is tested on a reference population in order to have an amount of data necessary for defining a model to estimate the biological age. The use of the presented system on a reference population, and the availability of data regarding their mobility, allow the validation of the proposed model for the computation of the biological age via simple and objective frailty phenotype assessment.

Maintenance cost is among the highest operational expenses for manufacturing firms. Proper scheduling of maintenance intervention results in optimized equipment life utilization, higher product quality, and reduced costs. For Cartesian Robot’s accuracy and precision it is important that the belt is well calibrated. Nonetheless, the manual assessment of calibration requires to stop the robot, which in turns causes the stop of the production with related consequences. In this work we are going to develop a Machine Learning based Classification Model, able to use cycle current consumption data of a Cartesian Robot in order to understand if the drive belt is calibrated or not. The trained model will be tested on completely new data whose label is known, to ensure its reliability.

It is a well-known fact that maintenance cost inside a company can be the largest part of operational expenses, second only to energy. Usually, replacing a component or equipment just before a breakdown occurs is the best way to minimize the maintenance cost. This is the main reason why a lot of companies are struggling in collecting data from equipment, and in finding ways to exploit these data for predictive purposes. In this paper we are going to explore multiple sensors’ data extracted from an injection moulding machine, with the final aim of developing a Predictive Maintenance model tailored on the specific machine utilization. After the extraction of a training set, we implemented Machine Learning algorithms in order to find the best predictive model able to discern between correct functioning and border line functioning of the machine. We are going to describe the performance reached by the developed model and to show how it deals with completely new data used for testing the model.

Customers are asking more and more customized products and expect to receive them in really short times. That is only reachable if there is horizontal and vertical integration, together with high information availability and transparency inside a company. When the production is not fully automatized, i.e. in those companies where the assembly or the production still relies on manual work of people, the monitoring of the line production, in terms of number of pieces produced, may be tricky due to the inevitable variability that operators add to the process, thus making essential the creation of smart systems able to deal with such complex environments and autonomously monitor them. Computer vision systems can be customized and very smart in various contexts, if properly modeled. In this paper we are going to describe the developed Machine Vision algorithm that, building upon BLOB (Binary Large Objects) analysis, is able to detect and count objects produced in a complex industrial context of manual assembly. The developed algorithm would then be compared to a more robust one taken as reference point for a comparison. The comparison between our algorithm and the Machine Learning-based Detector aims at showing the comparable Accuracy, Specificity and Sensitivity of our method, together with its higher versatility and processing speed, thus making it applicable in the plant-wide real-time monitoring of the manual assembly lines.

An accurate clinical assessment of the flexion-relaxation phenomenon on back muscles requires objective tools for the analysis of surface electromyography signals correlated with the real movement performed by the subject during the flexion-relaxation test. This paper deepens the evaluation of the flexion-relaxation phenomenon using a wireless body sensor network consisting of sEMG sensors in association with a wearable device that integrates accelerometer, gyroscope, and magnetometer. The raw data collected from the sensors during the flexion relaxation test are processed by an algorithm able to identify the phases of which the test is composed, provide an evaluation of the myoelectric activity and automatically detect the phenomenon presence/absence. The developed algorithm was used to process the data collected in an acquisition campaign conducted to evaluate the flexion-relaxation phenomenon on back muscles of subjects with and without Low Back Pain. The results have shown that the proposed method is significant for myoelectric silence detection and for clinical assessment of electromyography activity patterns.

The Industry 4.0 paradigm is based on transparency and co-operation and, hence, on monitoring and pervasive data collection. In highly standardized contexts, it is usually easy to gather data using available technologies, while, in complex environments, only very advanced and customizable technologies, such as Computer Vision, are intelligent enough to perform such monitoring tasks well. By the term &ldquo, complex environment&rdquo, we especially refer to those contexts where human activity which cannot be fully standardized prevails. In this work, we present a Machine Vision algorithm which is able to effectively deal with human interactions inside a framed area. By exploiting inter-frame analysis, image pre-processing, binarization, morphological operations, and blob detection, our solution is able to count the pieces assembled by an operator using a real-time video input. The solution is compared with a more advanced Machine Learning-based custom object detector, which is taken as reference. The proposed solution demonstrates a very good performance in terms of Sensitivity, Specificity, and Accuracy when tested on a real situation in an Italian manufacturing firm. The value of our solution, compared with the reference object detector, is that it requires no training and is therefore extremely flexible, requiring only minor changes to the working parameters to translate to other objects, making it appropriate for plant-wide implementation.

The article describes a dataset of gait measures acquired to validate the use of wearable sensors in gait analysis since its measurements can be compared with those provided by the stereophotogrammetric system. The comparison with a gold standard in gait analysis makes the dataset useful for the development, testing and validation of algorithms for estimating gait parameters. The dataset contains measurements simultaneously acquired by the wearable sensors and the stereophotogrammetric system during an acquisition campaign performed on 5 healthy subjects (2 females and 3 males aged between 25 and 35 years). In the acquisition campaign the involved subjects carried out a motion task wearing the wearable sensors and reflective markers of the stereophotogrammetric system. In particular, the subjects wore in each foot a wearable sensor on the instep and a reflective marker on heel, first metatarsal head, fifth metatarsal head, and above the sensor, respectively. During the motion task each subject walked over an 11-meter long walkway according to its own course. The 5 subjects involved in the acquisition campaign performed 3 repetitions of the motion task, for a total of 15 trials in where the measures collected by wearable sensors and the stereophotogrammetric system can be compared.

© 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.Photopletismography (PPG) is a simple, low cost and noninvasive technique, implemented by pulse-oximeters to measures several clinical parameters, such as hearth rate, oxygen saturation (Spo{2}), respiration and other clinical diseases. Although monitoring of these parameters at rest does not present particular problems, processing PPG signals during intensive physical activity is still a challenge, due to the presence of motion artifacts that affect its true estimation. In our work, a novel time-frequency based algorithm is presented to properly reconstruct PPG signal during intensive physical activity with respect to the ECG signal reference. Starting from raw PPG and acceleration signals, the proposed algorithm initially removes motion artifacts, providing an accurate heart rate estimation. Subsequently, it reconstructs PPG waveform based on both the heart rate information previously computed and the optimal selection of frequency-domain components representing PPG signal. Evaluating our proposed method on a dataset containing signals acquired during high speed running, we found for heart rate estimation an average absolute error of 1.20 BPM and very similar frequency dynamics between the ECG reference and PPG reconstructed HRV time series from a physiological point of view based on visual inspection.

The article describes a dataset of doppler ultrasound audio tracks taken on a sample of 30 divers according to the acquisition protocol defined by the Divers Alert Network. The audio tracks are accompanied by a medical evaluation for the decompression sickness risk according to the Spencer's scale levels.During the acquisition campaign, each diver in the post-dive phase was subjected to a double doppler ultrasound examination of approximately 45 seconds each one in the precordial area using a Huntleigh FD1 Fetal doppler probe. The two measurements were separated by a time of 8–10 seconds necessary for carrying out specific physical exercises designed to free the bubbles trapped in the tissues. The audio tracks were stored without compression via the TASCAM DP-004 recorder and processed in order to eliminate the noise generated by the positioning of the probe and the time interval between the two measurements.The audio tracks recorded during the acquisition campaign have been evaluated by experts belonging to three independent blind teams in order to provide an assessment of the decompression sickness risk according to Extended Spencer's scale. The specific typology of doppler ultrasound audio tracks and the associated medical evaluation according to the Spencer's scale levels make this dataset useful for the development, testing, and performance evaluation of new audio processing algorithms capable of automatically detecting bubbles in the blood vessels. Keywords: Embolic detection, Decompression sickness, Bubble detection, Doppler ultrasound automatic analysis

We propose a low-cost and low-power consumption device for seismic monitoring consisting of three single-axis accelerometers connected to a data logger with acquisition, synchronization, and transmission functionalities. The device was designed to be densely and prolifically deployed in high seismic risk areas, thus strengthening the Italian seismic network and providing a more accurate estimation of shaking maps. Moreover, the availability of such low-cost and high-performance units can allow the widespread diffusion of smart systems for seismic and structural monitoring, finalized to collapses prevention in critical structures, such as schools and hospitals, as well as constitute the founding nucleus of early warning systems based on Internet of Things architectures. The realized station was submitted to a testing phase, placing it contiguously to a high-performance seismic station located in central Italy and responsible for national seismic monitoring. The test station, installed from September 2016 to March 2017, was able to record the significant and numerous earthquakes that devastated central Italy during this period. The simultaneous acquisition of these seismic events by the sensors of the national seismic network, including that co-sited with the device under test, has furnished sufficient data for the device validation and performance quantification. A comparative analysis was performed through waveforms correlations study, strong motion parameters estimation, and spectral analysis. The proposed device demonstrated performances very close to those of more sophisticated and expensive systems. Therefore, it can effectively replace them or be added in engineering and civil protection applications and, finally, be used in earthquake early warning systems.

The corrosion behaviour of bare and galvanized steel reinforcements in geopolymeric and cementitious mortars, at three strength classes, has been investigated throughout the curing period and exposure to wet-dry cycles in 3.5% NaCl solution. During the curing, the high alkalinity of geopolymers prolongs the active state of both bare and galvanized steel. During the chloride exposure, fly ash geopolymers give the highest protection to reinforcements. The higher alkalinity of geopolymers compared to cement mortars seems to decrease the minimum free chloride threshold necessary to induce corrosion for galvanized steel, but it increases that for bare steel.

Rivers close to populated or strategically important areas can cause damages and safety risks to people in the event of a flood. Traditional river flood monitoring systems like radar and ultrasonic sensors may not be completely reliable and require frequent on-site human interventions for calibration. This time-consuming and resource-intensive activity has attracted the attention of many researchers looking for highly reliable camera-based solutions. In this article we propose an automatic Computer Vision solution for river’s water-level monitoring, based on the processing of staff gauge images acquired by a V-IoT device. The solution is based on two modules. The first is implemented on the edge in order to avoid power consumption due to the transmission of poor quality frames, and another is implemented on the Cloud server, where the frames acquired and sent by the V-IoT device are processed for water level extraction. The proposed system was tested on sample images relating to more than a year of acquisitions at a river site. The first module of the proposed solution achieved excellent performances in discerning bad quality frames from good quality ones. The second module achieved very good results too, especially for what it concerns night frames.

The proposed dataset provides a complete set of simultaneously acquired data from contactless and wearable devices for direct and indirect heart-rate measurement. Data were acquired on a total of 20 healthy white Caucasian subjects wearing no makeup (10 males and 10 females; age: 22.50 ± 1.57 years; height: 173 ± 10 cm; weight: 62.80 ± 9.52 kg) and consisted of: i) videos of the subject's face acquired by a RGB-D (Red, Green, Blue and Depth) camera (Microsoft Kinect v2), which is a contactless device; ii) electrocardiographic (ECG) recordings acquired by a clinical Holter ECG recorder (Global Instrumentation's M12R Holter), which is a wearable device; and iii) heart-rate measurements acquired from a commercial smartwatch (Moto 360 smartwatch by Motorola), which is also a wearable device. ECG recordings were processed to extract the R-peaks position and obtain a reference indirect measurement of the heart rate. A direct measurement of the heart rate was provided by the commercial smartwatch. The dataset here presented could be useful to develop new algorithms for heart-rate detection from contactless devices and to validate contactless heart-rate estimation in comparison to reference heart rate from clinical wearable devices and to heart rate from commercial wearable devices. Keywords: Heart-rate measurement, Contactless sensing, RGB-D sensors, Holter electrocardiography, Wearable sensor

Digital LED drivers capable of blending the spectrum of two LED fixtures with different Correlated Color Temperatures through the LEDs&rsquo, power supply control are widespread. However, the digital control of lighting systems is possible only after a careful study of the LED&rsquo, s response, in terms of illuminance and Correlated Color Temperature. The proposed work takes advantage of the Tunable White technology for the realization of an efficient method for LED light source characterization. In order to evaluate how the light changes as a function of the electric power supplied by the LED drivers, an experimental setup to characterize LED light sources has been designed. Starting from the data acquired from the experimental setup, a model for dimming the LED driver and obtaining the desired values of quality of light has been developed. The proposed model is based on the least squares method and its accuracy is evaluated by comparing the obtained values of illuminance and Correlated Color Temperature with those measured by an illuminance spectrophotometer. Results achieved an error of 0.3% for Correlated Color Temperature and 1.5% for illuminance using the proposed approximation functions.

Wearable electrocardiogram (ECG) sensing devices are defining the future of continuous, long-term and automatic cardiac health monitoring. The target problem for ECG acquisition and processing through patch-based wearable sensors is the noise induced by motion artifacts during daily life activities. In this work we propose an improvement of segmented beat modulation method (SBMM) with dynamic template to denoise ECG record acquired from a patch-based ECG sensor tested on healthy subjects while performing different activities: drinking coffee, typing keyboard, pressing and releasing signal electrode, walking at 1 and 3 miles per hour and running at 5 and 7 miles per hour inducing diverse motion artifacts. The robustness of the proposed algorithm is reported in terms of improvement in signal-to-noise ratio (SNR) corresponding to high, medium and low levels of input signal noise. Results stratified by daily activities indicate that dynamic-template SBMM filtering yields an overall improvement in SNR for ECG signals corrupted by motion artifacts supporting the hypothesis that the dynamic-template SBMM is an efficient denoising algorithm for ECG signal processing acquired through wearable sensors.

The world population ageing and the increase of health costs make necessary the development of new technologies and IoT solutions. Smart and wearable systems can improve people's lives, monitoring the elderly and transforming everyday living environments into more connected and sustainable systems. Due to ageing, people tend to suffer more and more from motor problems, so gait analysis is fundamental for many medical and health applications. In this paper, we propose a wearable sensors system for gait analysis constituted of a triaxial accelerometer, gyroscope, and magnetometer with efficient gait analysis algorithms. In this study we have developed an algorithm for the estimation of percentage duration of double support, stride length and stride speed. The results obtained from comparison with the gold standard show errors of 4.3 % for double support duration, 5.9 % for stride length, and 6.3 % for stride speed.

Divers&rsquo, health state after underwater activity can be assessed after the immersion using precordial echo Doppler examination. An audio analysis of the acquired signals is performed by specialist doctors to detect circulating gas bubbles in the vascular system and to evaluate the decompression sickness risk. Since on-site medical assistance cannot always be guaranteed, we propose a system for automatic emboli detection using a custom portable device connected to the echo Doppler instrument. The empirical mode decomposition method is used to develop a real-time algorithm able to automatically detect embolic events and, consequently, assess the decompression sickness risk according to the Spencer&rsquo, s scale. The proposed algorithm has been tested according to an experimental protocol approved by the Divers Alert Network. It involved 30 volunteer divers and produced 37 echo Doppler files useful for the algorithm&rsquo, s performances evaluation. The results obtained by the proposed emboli detection algorithm (83% sensitivity and 76% specificity) make the system particularly suitable for real-time evaluation of the decompression sickness risk level. Furthermore, the system could also be used in continuous monitoring of hospitalized patients with embolic risks such as post surgery ones.

In this paper we present an IPv6 Internet of Things Wireless Sensor Network whose complete architecture has been designed and developed by our research group. Every node of this Internet of Things solution is configured with its own global IPv6 address so making possible direct connections and queries to each node. A number of these nodes have been deployed with success in the Scrovegni Chapel heritage site (Padua, Italy) to monitor and control the lighting of the Giotto's frescoes. To analyze the performances of such IoT network we have previously developed an analog Wireless Sensor Network composed of the same type of nodes and deployed in a different indoor environment. We have used this network to measure the packet loss, round trip time and goodput over the IEEE 802.15.4 channel for 6LoWPAN packets. To the best of our knowledge, this is the first operating Italian IPv6 global access Wireless Sensor Network.

Running is the most common physical activity. Being an aerobic activity, it can act as a trigger for critical cardiac events that may degenerate in sport-related sudden cardiac death. Nowadays, smartphone applications combined with wearable sensors are typically used to monitor runner's performance during training, but almost never to evaluate their cardiac risk conditions. Thus, aim of this study was to propose CaRiSMA as a useful Android application for self-monitoring of cardiac activity of runners while wearing a cardiac sensor and running by strictly following a route around the city of Ancona (6.1 Km). Cardiac data from 10 young runners were recorded and transferred to a smartphone to be analyzed by CaRiSMA, an Android application that provides two traffic lights as output, relative to cardiac health status of the runner and correctness of training intensity. The first traffic light was green in all cases but one for which it was yellow, indicating no risk and increased risk conditions, respectively. The second traffic light was yellow in all cases, suggesting a reduction of the training intensity. In conclusion, CaRiSMA demonstrated to be a potentially useful Android application for self-monitoring of cardiac activity of runners while wearing a cardiac sensor.

This paper presents an IoT architecture for continuous and real-time building monitoring. The proposed solution is based on a sensor node positioned inside the building to be monitored, connected to the Internet, able to perform continuous measurements and to real-time send raw data to a remote server through the MQTT protocol. Thanks to the development of a specific Web application, each authorized user can access the real-time information on the health status of the structure, the history of the measurements and, if necessary, to send commands to the sensor node. The goal is to provide a low-cost, reliable and replicable system for the implementation of a widespread sensors network for structural monitoring.

Internet of things devices are used to collect data from the physical world, and to present the results in a way well usable to the end user. Therefore, an accurate choice of the most appropriate technology for storing data collected from the network is relevant. In this paper we focus the attention on the selection of the best database management system for cultural heritage applications, in particular referring to the use case of light monitoring at the Scrovegni Chapel (Padua, Italy), to emphasize the Giotto’s frescoes. For doing so, SQL and NoSQL solutions are compared, and the obtained results are used to find the best solution for this application. Moreover these results can be used as a practical reference for the more appropriate selection of the right database for real use cases.

Fog and cloud computing are very attractive enabling technologies for Internet of Things applications. The virtualization of smart devices which compose the Internet of Things network is a promising field of research, leading to the design and development of new services and uses. To the best of our knowledge, this paper presents the first attempt to use a cybersecurity attack to realize a fog computing agent for Internet of Things systems. In particular the Man-In-The-Middle attack will be use to show the foundation of the proposed Man-In-the-Middle-Iot-Computing tool, namely MIMIC, and through the examples and tests performed we will show its potentialities.

The human body has two different ages: a Chronological Age (CA), the actual time a person has been alive, and a Biological Age (BA), the real age that indicate the decline in health and in function ability during aging. Among previous studies, some authors proposed methodologies to estimate the Biological Age starting from non-invasive frailty measurements to evaluate the Frailty Index, others proposed invasive and expensive methods to measure the biological aging. Conversely, in this paper we propose a method to estimate the BA of a subject based on the assessment of the Frailty Phenotype. This type of evolution allows an efficient estimation of the frailty in contrast with the Frailty Index which is composed by a long checklist of clinical conditions and diseases to be evaluated from medical staff. We developed a cloud application, able to store and elaborate the data acquired during the evaluation protocol of the Frailty Phenotype, and also able to automatically provide the state of phenotypic fragility, and finally the Biological Age of a subject.

In clinical practice the ability to monitor arrhythmia episodes in elderly people is helpful to make an accurate diagnosis and choose the proper therapeutic interventions to reduce potential health risk. In this paper we propose an eHealth system to detect atrial fibrillation events as well as provide information about patient’s health status using commercial devices such as a smartphone and a wearable sensor for heart rate monitoring. Our solution consists of a smartphone application able to real time process raw data from the wearable sensor, detect critical events for the patient’s health status, and generate remote alert to medical staff. In the smartphone application a SVM-based algorithm to detect arrhythmia episodes by handling electrocardiogram signal is implemented. To test the performance of the developed eHealth system, the proposed algorithm has been evaluated using acquisitions with atrial fibrillation events. The results show a sensitivity of 94% and a specificity of 93%.

Carbon-based fillers from industrial wastes and commercial ones were compared to improve the properties of lime-based mixes. As commercial fillers, graphene nanoplatelets and activated carbon were used, whereas as industrial wastes a char obtained by the gasification of biomasses and a used foundry sand were chosen. Carbon-based wastes were found to be a good cost-effective alternative to commercial carbon based fillers to increase the compressive strength (of about 25%) and to reduce water capillary absorption (of about 50%) thanks to the paste refinement; to enhance depollution capacity (of about 25%) and increase both electrical conductivity (up to 65%) and electromagnetic shielding effectiveness (of about 6%) of the hardened compounds thanks to the carbon content.

Alkali-activated and cementitious mortars belonging to R1 ≥ 10 MPa, R2 ≥ 15 MPa and R3 ≥ 25 MPa strength classes were tested and compared in terms of workability, dynamic modulus of elasticity, porosimetry, and water vapor permeability. Capillary water absorption, drying shrinkage, resistance to sulfate attack, and corrosion behavior of embedded bare and galvanized reinforcements were also investigated. In alkali-activated mortars, drying shrinkage is higher than that of cementitious mortars but restrained shrinkage is lower due to lower modulus of elasticity. Pore dimensions affect water vapor permeability, more pronounced in alkali-activated mortars, and capillary water absorption, much lower in fly ash ones. The high alkalinity of fly ash and metakaolin mortars delayed the achievement of the passive state in particular for the galvanized reinforcements but after 1 month of curing they reached the same corrosion rates of those embedded in cementitious mortars.

Falls and their consequences are among the major health care problems affecting functional mobility and quality of life of elderly people. Even for people living independently, falls are common occurrences. In this paper, we present a waist-mounted device useful to detect possible falls in elderly people. Through data coming from a three-axis accelerometer, a three-axis gyroscope, a three-axis magnetometer, and a barometer sensor integrated into our device, we are able to obtain a highly accurate estimation about posture and altitude of the subject. By means of such information, we have developed an extremely efficient system for fall detection, reaching 100% of sensitivity in commonly adopted testing protocols. In particular, the algorithm was tested according to three different experimental protocols, where volunteers performed several scenarios, including various types of falls, falls with recovery, and daily living activities frequent in the elderly. Results show that the proper combined use of the four sensors and efficient data fusion algorithms allow to achieve noticeable better performances to those obtained with similar systems proposed in the literature.

The relationship between flexibility and the pattern formed by the surface electromyography activity of the back muscles while performing a dynamic trunk flexion-extension task is not yet thoroughly understood, although many previous studies have adopted it as their focus in the literature. Additionally, several studies have proposed technologies and algorithms to analyse the flexion-relaxation phenomenon, which is defined by myoelectric silence that occurs when the subject's torso exceeds a certain flexion angle. Before participating in the flexion-relaxation test, subjects involved in the data collection underwent medical examinations, in which their physical condition, perceived pain, and level of disability were reported in their anamnesis. During the flexion-relaxation test, which was conducted with 25 subjects with and without low back pain, subjects wore four surface electromyography electrodes positioned over the back muscles, as well as an inertial sensor to estimate trunk inclination.

The Light Emitting Diode (LED) has many advantages compared to traditional lamps, such as a long lifetime, color rendering and energy saving. It requires good thermal management, since as the temperature increases, the lifetime decreases. Furthermore, the presence of cracks in the Solder Joint of an LED (SJL) compromises the correct dispersion of heat and causes the joint fatigue. This can lead to a decrease in the lifetime of the assembled LED. In this study, we validated that an SJL can be considered faulty if the Forward Voltage (Vf) acquired before and after thermal cycles increases by more than 2%. The voltage measurement method was validated by comparing the results with the techniques commonly used to evaluate the defects of a solder joint as the X-ray analysis and the metallographic section. The failure analysis results present the probability of failure and the lifetime of the SJL achieved by analyzing the data using the Norris&ndash, Landberg Model. The lifetime calculated over 1800 SJLs considered in the validation process is greater than 20 years for 95.9% of the tested LEDs.

The paper aims to provide a comprehensive study on the compositional optimization of high-conductive multifunctional fiber-reinforced cement mortars (FRCMs). Therefore, the effects of three different fiber types: virgin carbon fibers (VCFs), recycled carbon fibers (RCFs), and brass-plated steel fibers (BSFs), added at a broad range of concentrations, as 0.05%, 0.1%, 0.2%, 0.4%, 0.8%, 1.2%, and 1.6% by volume, on the mechanical, electrical and durability properties of FRCMs have been compared. The results showed that RCFs increase the flexural and tensile splitting strength up to 100%, whereas BSFs improve the compressive strength by 38%. Moreover, the fibers decrease both the capillary water absorption and the drying shrinkage by 39%. Electrical conductivity tests show that RCFs decrease the electrical resistivity of mortars up to one order of magnitude, in addition to a percolation threshold between 0.1 and 0.2 vol%.

In this paper a medium-high performance wireless sensor node based on low-cost Micro Electro-Mechanical Systems (MEMS) accelerometers for Structural Health Monitoring (SHM) applications is proposed. The objective of the work is the estimation of the performances of the low cost MEMS system for SHM with a possible application on a pervasive permanent monitoring of buildings in seismic prone regions often affected by earthquakes. The work aims at improving the sensing technology involved in the dynamic identification and SHM, with the intent to reduce the overall cost of acquisition's system and to make faster the tests procedure. Preliminary experimental tests performed to compare the performance of the proposed system with those of a typical high-performance and expensive wired system, are presented and discussed.

The paper represents the “state of the art” on sustainability in construction materials. In Part 1 of the paper, issues related to production, microstructures, chemical nature, engineering properties, and durability of mixtures based on binders alternative to Portland cement were presented. This second part of the paper concerns the use of traditional and innovative Portland-free lime-based mortars in the conservation of cultural heritage, and the recycling and management of wastes to reduce consumption of natural resources in the production of construction materials. The latter is one of the main concerns in terms of sustainability since nowadays more than 75% of wastes are disposed of in landfills.

This review presents “a state of the art” report on sustainability in construction materials. The authors propose different solutions to make the concrete industry more environmentally friendly in order to reduce greenhouse gases emissions and consumption of non-renewable resources. Part 1—the present paper—focuses on the use of binders alternative to Portland cement, including sulfoaluminate cements, alkali-activated materials, and geopolymers. Part 2 will be dedicated to traditional Portland-free binders and waste management and recycling in mortar and concrete production.

Falls are critical events among elderly people that requires timely rescue. In this paper, we propose a fall detection system consisting of an inertial unit that includes triaxial accelerometer, gyroscope, and magnetometer with efficient data fusion and fall detection algorithms. Starting from the raw data, the implemented orientation filter provides the correct orientation of the subject in terms of yaw, pitch, and roll angles. The system is tested according to experimental protocols, engaging volunteers who performed simulated falls, simulated falls with recovery, and activities of daily living. By placing our wearable sensor on the waist of the subject, the unit is able to achieve fall detection performance above those of similar systems proposed in literature. The results obtained through commonly adopted protocols show excellent accuracy, sensitivity and specificity, improving the results of other techniques proposed in the literature.

The Internet of Things includes all the technologies allowing the connection of everyday objects to the Internet, in order to gather measurements of physical quantities and interact with the surrounding environments through telecommunication devices with embedded sensing and actuating units. The measurements carried out with different LEDs demonstrate the possibility of using these devices both as transmitters and as optical sensors, in addition to their ability to discriminate incident wavelengths, thus making them bi-directional transceivers for Internet of Things (IoT) applications, particularly suitable in the context of Visible Light Communication (VLC). In particular, a methodological tool is provided for selecting the LED sensor for VLC applications.

This paper deals with the study of calcium sulfoaluminate (CSA) and geopolymeric (GEO) binders as alternatives to ordinary Portland cement (OPC) for the production of more environmentally-friendly construction materials. For this reason, three types of mortar with the same mechanical strength class (R3 ≥ 25 MPa, according to EN 1504-3) were tested and compared; they were based on CSA cement, an alkaline activated coal fly ash, and OPC. Firstly, binder pastes were prepared and their hydration was studied by means of X-ray diffraction (XRD) and differential thermal-thermogravimetric (DT-TG) analyses. Afterwards, mortars were compared in terms of workability, dynamic modulus of elasticity, adhesion to red clay bricks, free and restrained drying shrinkage, water vapor permeability, capillary water absorption, and resistance to sulfate attack. DT-TG and XRD analyses evidenced the main reactive phases of the investigated binders involved in the hydration reactions. Moreover, the sulfoaluminate mortar showed the smallest free shrinkage and the highest restrained shrinkage, mainly due to its high dynamic modulus of elasticity. The pore size distribution of geopolymeric mortar was responsible for the lowest capillary water absorption at short times and for the highest permeability to water vapor and the greatest resistance to sulfate attack.

Contactless detection is one of the new frontiers of technological innovation in the field of healthcare, enabling unobtrusive measurements of biomedical parameters. Compared to conventional methods for Heart Rate (HR) detection that employ expensive and/or uncomfortable devices, such as the Electrocardiograph (ECG) or pulse oximeter, contactless HR detection offers fast and continuous monitoring of heart activities and provides support for clinical analysis without the need for the user to wear a device. This paper presents a validation study for a contactless HR estimation method exploiting RGB (Red, Green, Blue) data from a Microsoft Kinect v2 device. This method, based on Eulerian Video Magnification (EVM), Photoplethysmography (PPG) and Videoplethysmography (VPG), can achieve performance comparable to classical approaches exploiting wearable systems, under specific test conditions. The output given by a Holter, which represents the gold-standard device used in the test for ECG extraction, is considered as the ground-truth, while a comparison with a commercial smartwatch is also included. The validation process is conducted with two modalities that differ for the availability of a priori knowledge about the subjects' normal HR. The two test modalities provide different results. In particular, the HR estimation differs from the ground-truth by 2% when the knowledge about the subject's lifestyle and his/her HR is considered and by 3.4% if no information about the person is taken into account.

In the current paper, the effect on Indoor Air Quality (IAQ) of two commercial acrylic-based paints were compared: one (Paint A) for indoor applications, the other (Paint B) for indoor/outdoor applications. Both were applied on an inert and on a real mortar substrate. The possibility of Paint B to passively improve IAQ was also investigated when adding highly porous adsorbent fillers, both as addition or as total replacement of a conventional siliceous one. The obtained results show that all paints have high capacity to inhibit biological growth. Paint A is more breathable and it has a higher moisture buffering capacity. Paint B negatively modifies the beneficial properties of the mortar substrate for IAQ. However, the use of unconventional fillers, especially as addition to the formulation, allows the recovery of the same properties of the substrate or even the enhancement of about 20% of the ability to adsorb volatile organic compounds (VOCs) under the current test conditions.

This paper describes a dataset acquired on 8 subjects while simulating 13 types of falls and 5 types of Activities of Daily Living (ADL), each repeated 3 times. In details, data includes 4 simulated falls forward (falling on knees ending up lying, ending in lateral position, ending up lying, ending up lying with recovery), 4 backward (falling sitting ending up lying, ending in lateral position, ending up lying, ending up lying with recovery), 2 lateral right (ending up lying, ending up lying with recovery), 2 lateral left (ending up lying, ending up lying with recovery), and 1 syncope. Simulated ADL are: lying on a bed then standing; walking a few meters; sitting on a chair then standing; go up or down three steps; and standing after picking something. Data were acquired using a MARG sensor, a wearable multisensory device tied to the subject's waist, that recorded time-variations of the subject's acceleration and orientation (expressed through the yaw, pitch and roll angles). These data can be useful in the development and test of algorithms to automatically identify and classify fall events. Fall detection systems are particularly useful when a subject is alone and not able to stand up after a fall, since an automatic alarm can be sent remotely to receive proper help. Keywords: Human fall, Fall detection, MARG (Magnetic Angular Rate and Gravity) sensor, Wearable device

O CPC-16 (Comitê de Pronunciamentos Contábeis) consiste em uma norma, alinhada com a harmonização internacional contábil, que aborda a forma de controle dos estoques. A pesquisa analisa a adoção da CPC 16 e a relação com os princí­pios da governança corporativa, além de investigar a relevância para as pequenas e médias empresas. A metodologia deste trabalho, além de pesquisa bibliográfica, foi fundamentada na análise de cinco entrevistas roteirizadas, com questões abertas, respondidas por profissionais experientes da área contábil. Ao final foi observado que o CPC-16, assim como as Normas Brasileiras de Contabilidade estão alinhadas com os princí­pios da Governança Corporativa. The CPC-16 (Accounting Pronouncements Committee), consisting of a standard, in line with international accounting harmonization, which addresses how to control inventory. The research analyzes the adoption of CPC 16 and the relationship with the principles of corporate governance, and to investigate the relevance for small and medium enterprises. The methodology of this study, in addition to literature, was based on an analysis of five scripted interviews, with open questions, answered by professionals with recognized experience. At the end it was observed that the CPC-16, as well as the Brazilian Accounting Standards are aligned with the principles of Corporate Governance.