This paper presents a complete architecture of an innovative wireless sensor network able to real-time monitor and control complex lighting systems such as the newly installation at the Scrovegni Chapel (Padua, Italy), representing the first world example in cultural heritage field. The realization effectively creates the Internet of Things paradigm, since all its components are directly connected to the Internet and each other thanks to global IPv6 addresses. The new IoT lighting system represents a ”restoration of perception” project because the luminaries can be controlled in intensity and color temperature based on natural light and the desired rendering. In the initial phase, specifically designed sensors measure indoor lighting variations and this monitoring will be extended for a sufficient time to determine the proper control actions. Furthermore, besides adjusting the illuminance and color temperature for aesthetic and perceptual purposes, the system can also real-time monitor and control a variety of environmental parameters, fundamental for cultural heritage conservation, providing long term studies of correlations between artificial light, natural light and lighting performance, and also to evaluate over time the conservative aspects correlating them with any other critical environmental condition. The control of exposure to artificial light is also important in other contexts because light can interfere with biological processes controlled by endogenous circadian rhythms, with possible negative consequences for health results. Finally, this architectural solution can be extended to a complete Smart City system that integrates cultural heritage as one of the countless elements to be monitored and controlled in the city.

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.

Movement monitoring in patients with Parkinson’s disease (PD) is critical for quantifying disease progression and assessing how a subject responds to medication administration over time. In this work, we propose a continuous monitoring system based on a single wearable sensor placed on the lower back and an algorithm for gait parameters evaluation. In order to preliminarily validate the proposed system, seven PD subjects took part in an experimental protocol in preparation for a larger randomized controlled study. We validated the feasibility of our algorithm in a constrained environment through a laboratory scenario. Successively, it was tested in an unsupervised environment, such as the home scenario, for a total of almost 12 h of daily living activity data. During all phases of the experimental protocol, videos were shot to document the tasks. The obtained results showed a good accuracy of the proposed algorithm. For all PD subjects in the laboratory scenario, the algorithm for step identification reached a percentage error low of 2%, 99.13% of sensitivity and 100% of specificity. In the home scenario the Bland–Altman plot showed a mean difference of −3.29 and −1 between the algorithm and the video recording for walking bout detection and steps identification, respectively.

Digital LED drivers capable of blending the spectrum of two LED fixtures with different Correlated Color Temperatures through the LEDs’ power supply control are widespread. However, the digital control of lighting systems is possible only after a careful study of the LED’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.

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.

This paper presents the study of Nordic Walking providing objective evaluations based on real time acquisition of kinematic parameters during the sport practice. It is possible to carefully monitor the athletic gesture through the integration of conventional poles with inertial sensors, composed of a triaxial accelerometer, a triaxial gyroscope, a pressure sensor positioned on the handle, and a load cell, which constitute a Wireless Sensor Network whose nodes are appropriately synchronized. The integration of such sensors, which must be unobstructive and not change the functionality of the poles, is dictated by the ultimate goal of providing a real time biofeedback in two possible scenarios. The first is intended for Nordic Walking’s instructors, who have the opportunity to verify the proper practice execution by their trainees through the availability of real time objective data, in addition to their personal experience. The second is devoted to amateur players who can practice alone, after the training session with the instructor, and can independently correct any imperfections in real time using a software tool running on their smartphone. Using the Dynamic Time Warping algorithm, the proposed system identifies the most frequent errors in performing athletic gesture, allowing adjustment in real time of the sporting exercise, through the detection, quantification and correction of errors. The obtained results show that the developed system is able to provide an accurate analysis of the athletic gesture and the proposed algorithm allows a quantitative monitoring of the progress achieved by each subject over time.