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Fluxgate magnetometers are, possibly, the simplest and most convenient magnetic flux sensors and yet capable, in controlled environments and with some well-conceived technical embellishments, to detect magnetic fields in the order of 50 nT or less. In practice, they are limited by the presence of unwanted or contaminating signals, these include the sensor noise floor as well as noise-contamination of the target signal of interest. The residence time difference (RTD) readout technique was conceived as a readout protocol that is remarkable in its simplicity and its ability to outperform the conventional “second harmonic” readout. In this work, we address the issue of the fluxgate sensitivity in the presence of spurious dc magnetic field signals, while ensuring that the sensor delivers a high responsivity to the target signal. This is achieved through a “Horseshoe Configuration”: three rod-core fluxgates are connected in series forming a U (horseshoe) and the output is drawn only from one secondary (or pick-up) coil using the RTD readout mechanism. The two branches of this configuration yield a differential mechanism vis-a-vis the (unwanted) external magnetic field, while the magnetic target is “localized” by being placed in or in the proximity of the air gap of the horseshoe. A theoretical analysis of the efficacy of this configuration considering the geometry, the demagnetizing effect, and the sensing mechanism is carried out. The results confirm the theoretical assumptions: the sensitivity to the (spurious) external magnetic fields can be reduced and the sensitivity to the target signal enhanced, with a concomitant enhanced tolerance to noise. The results appear promising for the detection of very small magnetic fields, e.g., the magnetic fields encountered in the project “IRMA Parkinson Cyclone in Life” for the noninvasive diagnosis of neuroferritinopathies through the detection of a few milligrams of iron inside the brain.

The possibility of identifying potential altered postural status in frail people, including patients with Parkinson Disease, represents an important clinical outcome in the management of frail elderly subjects, since this could lead to greater instability and, consequently, an increased risk of falling. Several solutions proposed in the literature for the monitoring of the postural behavior use infrastructure-dependent approaches or wearable devices, which do not allow to distinguish among different kinds of postural sways. In this article, a low-cost and effective wearable solution to classify four different classes of postural behaviors (Standing, Antero-Posterior, Medio-Lateral, and Unstable) is proposed. The solution exploits a sensor node, equipped by a triaxial accelerometer, and a dedicated algorithm implementing the classification task. Different quantities are proposed to assess performance of the proposed strategy, with particular regards to the system capability to correctly classify an unknown pattern, through the index Q%, and the reliability index, RI%. Results achieved across a wide dataset demonstrated the suitability of the methodology developed, with Q% =99.84% and around 70% of classifications, showing an RI% above 65%.

Energy Harvesting strategies coupled with the improvement of electronics and the progressive reduction of power requirements have been widely recognized as fundamental to enable self-powered (or autonomous) devices. Among all the potential energy sources, kinetic energy stemming from mechanical vibrations has been particularly extensively investigated for EH purposes due to its characteristics of heterogeneity and ubiquity. To exploit such energy sources, a suitable coupling mechanism to convert vibrations into electric charge is required; it must take into account the wide frequency bandwidth of mechanical vibrations as encountered in everyday scenarios. This review offers an overview of linear vs. non-linear strategies for EH, with a specific focus on different approaches to implement efficient coupling mechanisms; the performances of the specific solutions covered in this work are discussed.

With the support of public authorities and research institutions, volcanic ash fallout and its impact on the safety of people, infrastructure and services are addressed with the aim of defining protocols and instruments for the reliable and effective handling of related emergencies. Most of the solutions proposed in the literature on ash fallout monitoring suffer from high cost and are demanding in terms of installation and maintenance. The approach suggested in this work is based on the use of a low-cost vision embedded system and a dedicated algorithm which automatically processes acquired frames of ground-deposited volcanic ash in order to estimate the main geometric properties of each particle identified in the work area. A complete characterization of the system is presented, along with a robustness analysis of particle shapes, their orientation and their position in the inspected frame. An accuracy of ±40.2 µm (with a 3σ limit) and a resolution of 32.9 µm (in the worst case), over a framed area of 130 mm by 100 mm, were estimated; these values would fulfill the objectives of the application.

Assistive Technology helps to assess the daily living and safety of frail people, with particular regards to the detection and prevention of falls. In this paper, a comparison is provided among different strategies to analyze postural sway, with the aim of detecting unstable postural status in standing condition as precursors of potential falls. Three approaches are considered: (i) a time-based features threshold algorithm, (ii) a time-based features Neuro-Fuzzy inference system, and (iii) a Neuro-Fuzzy inference fed by Discrete-Wavelet-Transform-based features. The analysis was performed across a wide dataset and exploited performance indexes aimed at assessing the accuracy and the reliability of predictions provided by the above-mentioned strategies. The results obtained demonstrate valuable performances of the three considered strategies in correctly distinguishing among stable and unstable postural status. However, the analysis of robustness against noisy data highlights better performance of Neuro-Fuzzy inference systems with respect to the threshold-based algorithm.

The huge number of electronic devices called the Internet of Things requires miniaturized, autonomous and ecologically sustainable power sources. A viable way to power these devices is by converting mechanical energy into electrical through electro-active materials. The most promising and widely used electro-active materials for mechanical energy harvesting are piezoelectric materials, where the main one used are toxic or not biocompatible. In this work, we focus our attention on biocompatible and sustainable piezoelectric materials for energy harvesting. The aim of this work is to facilitate and expedite the effort of selecting the best piezoelectric material for a specific mechanical energy harvesting application by comprehensively reviewing and presenting the latest progress in the field. We also identify and discuss the characteristic property of each material for each class to which the material belong to, in terms of piezoelectric constants and achievable power.

In this paper, we present integrated lead-free energy converters based on a suitable MEMS fabrication process with an embedded layer of LiNbO3. The fabrication technology has been developed to realize micromachined self-generating transducers to convert kinetic energy into electrical energy. The process proposed presents several interesting features with the possibility of realizing smaller scale devices, integrated systems, miniaturized mechanical and electromechanical sensors, and transducers with an active layer used as the main conversion element. When the system is fabricated in the typical cantilever configuration, it can produce a peak-to-peak open-circuit output voltage of 0.208 V, due to flexural deformation, and a power density of 1.9 nW·mm−3·g−2 at resonance, with values of acceleration and frequency of 2.4 g and 4096 Hz, respectively. The electromechanical transduction capability is exploited for sensing and power generation/energy harvesting applications. Theoretical considerations, simulations, numerical analyses, and experiments are presented to show the proposed LiNbO3-based MEMS fabrication process suitability. This paper presents substantial contributions to the state-of-the-art, proposing an integral solution regarding the design, modelling, simulation, realization, and characterization of a novel transducer.

Volcanic ash fall-out represents a serious hazard for air and road traffic. The forecasting models used to predict its time–space evolution require information about the core characteristics of volcanic particles, such as their granulometry. Typically, such information is gained by the spot direct observation of the ash collected at the ground or by using expensive instrumentation. In this paper, a vision-based methodology aimed at the estimation of ash granulometry is presented. A dedicated image processing paradigm was developed and implemented in LabVIEW™. The methodology was validated experimentally using digital reference images resembling different operating conditions. The outcome of the assessment procedure was very encouraging, showing an accuracy of the image processing algorithm of 1.76%.

The problem of estimating the indoor position of a person or an object, also known as indoor localization, has gained a lot of interest in the last decades. Actually, this feature would be valuable in many application contexts, from logistics to robotic and Assistive Technology. Different solutions have been proposed in the literature, exploiting a wide range of approaches. This paper aims to provide a brief review of the state-of-the-art approaches in the field, as well as to present the RESIMA case study. The latter exploits an ultrasound-based indoor localization system and a User–Environment Interaction functionality, which allows for performing the continuous estimation of the distance between the end-user and objects in the environment. The latter is valuable to provide the end-user with efficient assistance during the environment exploitation. The main focus of this work is related to the overall description of the system architecture, the trilateration algorithm adopted for the sake of user localization and the estimation of the delay time produced by user-distance computation under different operating conditions.

The development of low-cost mass sensors is of unique interest for the scientific community due to the wide range of fields requiring these kind of devices. In this paper, a full inkjet-printed mass sensor is proposed. The device is based on a PolyEthylene Terephthalate (PET) cantilever beam (operating in its first natural frequency) where a strain-sensor and a planar coil have been realized by a low-cost InkJet Printing technology to implement the sensing and actuation strategies, respectively. The frequency readout strategy of the sensor presents several advantages, such as the intrinsic robustness against instabilities of the strain sensor, the residual stress of the cantilever beam, the target mass material, and the distance between the permanent magnet and the actuation coil (which changes as a function of the target mass values). However, the frictionless actuation mode represents another shortcoming of the sensor. The paper describes the sensor design, realization, and characterization while investigating its expected behavior by exploiting dedicate models. The working span of the device is 0–0.36 g while its resolution is in the order of 0.001 g, thus addressing a wide range of potential applications requiring very accurate mass measurements within a narrow operating range.

This paper, for the first time, investigates the possibility of exploiting a nonlinear bistable snap-through buckling structure employing piezoelectric transducers, to implement an autonomous sensor of mechanical vibrations, with an embedded energy harvesting functionality. The device is operated in the presence of noisy vibrations superimposed on a subthreshold deterministic (sinusoidal) input signal. While the capability of the device to harvest a significant amount of energy has been demonstrated in previous works, here, we focus on the signal processing methodology aimed to extract from the sensor output the information about the noise level (in terms of the standard deviation) and the root mean square amplitude of the deterministic component. The developed methodology, supported by experimental evidence, removes the contribution to the overall piezoelectric output voltage ascribable to the deterministic component using a thresholding and windowing algorithm. The contribution to the output voltage due to the noise can be used to unambiguously estimate the noise level. Moreover, an analytical model to estimate, from the measurement of the output voltage, the RMS amplitude of the deterministic input and the noise-related component is proposed.

We developed a novel silicon biosensor based on magnetoelectronic transduction (MAGS) for nucleic acid detection. The mags-biosensor is a planar device composed by a primary micro-coil, and two secondary coils which produce a differential voltage due to the induced magnetic field. The presence of magnetic material over one of the secondary coils causes variations of induced magnetic field density that in turn results in a total output voltage different from zero. The voltage variation, therefore, is a measure of the amount of magnetic material present in the active zone. A device sensitivity of 5.1 mV/ng and a resolution of 0.008 ng have been observed. The biosensor also presents a micro-heater and a thermal sensor respectively to set and read-out the chip temperature: this aspect enables the device to be used for several biochemical applications that need temperature control and activation such for example nucleic acid amplification (real-time PCR), antigen- antibody detection (immune-assay) and SNP detection. Keywords: Magnetic transduction, Nucleic acid amplification, Micro-heater, Temperature control

Population ageing is having a direct influence on serious health issues, including hampered mobility and physical decline. Good habits in performing physical activities, in addition to eating and drinking, are essential to improve the life quality of the elderly population. Technological solutions, aiming at increasing awareness or providing reminders to eat/drink regularly, can have a significant impact in this scenario. These solutions enable the possibility to constantly monitor deviations from users’ normal behavior, thus allowing reminders to be provided to users/caregivers. In this context, this paper presents a radio-frequency identification (RFID) system to monitor user’s habits, such as the use of food, beverages, and/or drugs. The device was optimized to fulfill specifications imposed by the addressed application. The approach could be extended for the monitoring of home appliances, environment exploitation, and activity rate. Advantages of the approach compared to other solutions, e.g., based on cameras, are related to the low level of invasiveness and flexibility of the adopted technology. A major contribution of this paper is related to the wide investigation of system behavior, which is aimed to define the optimal working conditions of the system, with regards to the power budget, user (antenna)-tag reading range, and the optimal inter-tag distance. To investigate the performance of the system in tag detection, experiments were performed in a scenario replicating a home environment. To achieve this aim, specificity and sensitivity indexes were computed to provide an objective evaluation of the system performance. For the case considered, if proper conditions are meet, a specificity value of 0.9 and a sensitivity value of 1 were estimated.

In this paper a new optical, Fiber Bragg Grating (FBG) based, pressure sensor is presented. The sensor adopts a stainless steel membrane with a FBG sensor attached and is thought to be used in a multi parametric multi-sensor system aimed at the detection of water leakages in potable water networks. The sensor has been experimentally characterized on a reference plant in the range of pressure 0–6 bar. Main metrological characteristics of the lab-scale prototype developed are: a sensitivity of 0.314 nm/bar, an accuracy of about 39 mbar and a resolution of about 1.5 mbar.

Ageing is a global phenomenon which is pushing the scientific community forward the development of innovative solutions in the context of Active and Assisted Living (AAL). Among functionality to be implemented, a major role is covered by falls and human activities monitoring. In this paper, main technological solutions to cope with the aforementioned needs are briefly introduced. A specific focus is given on solutions for Falls recognition and classification. A case of study is presented, where a classification methodology based on an event-driven correlation paradigm and an advanced threshold-based classifier is addressed. The receiver operating characteristic (ROC) theory is used to properly define thresholds’ values while, in order to properly assess performances of the classification methodology proposed, dedicated metrics are suggested, such as sensitivity and specificity. The solution proposed shows an average Sensitivity of 0.97 and an average Specificity of 0.99.

In this paper, we present a redundant microsensor based on the bulk and etch silicon‑on‑insulator (BESOI) process for measuring relative humidity (RH), by using a graphene‑oxide/polyvinyl‑alcohol (GO/PVA) composite. The MEMS is a mechanical oscillator, composed of a proof mass with multilayer of nanomaterials (GO/PVA) and suspended by four crab-leg springs. The redundant approach realized concerns the use of different readout strategies in order to estimate the same measurand RH. This is an intriguing solution to realize a robust measurement system with multiple outputs, by using the GO/PVA as functional material. In the presence of RH variation, GO/PVA (1) changes its mass, and as consequence, a variation of the natural frequency of the integrated oscillator can be observed; and (2) varies its conductivity, which can be measured by using two integrated electrodes. The sensor was designed, analyzed and modeled; experimental results are reported here to demonstrate the effectiveness of our implementation.

The use of inexpensive gas sensors is a real need for many applications requiring the use of disposable sensors. This work deals with the realization and characterization of a low cost CO2 sensor realized by rapid prototyping techniques. In particular, the sensor consists of a set of InterDigiTed electrodes, over which a double sensing layer made of PEDOT/PSS (CLEVIOS™ PHCV4, by H.C.Starck) and a solution of pristine graphene powder has been deposited. A silver nano-particle solution is used for inkjet printing the electrodes onto the PET (PolyEthylene Terephthalate) substrate, through a commercial inkjet printer. The sensing strategy is based on the variation of the electrical conductance of graphene due to gas molecules adsorption. The device responsivity observed in two different operating conditions (50 °C and 60 °C), is 4.0 µΩ/Ω/ppm and 4.7 µΩ/Ω/ppm. The corresponding values of the resolution are 400 ppm and 420 ppm. Main advantages of the developed sensor consist in the cost-effective fabrication techniques and the device flexibility, which are strategic for applications requiring disposable and shapeable devices to be installed into irregular surfaces.

In this paper, we present a redundant micromachined sensor based on Bulk and Etch. Silicon-on-Insulator (BESOI) process for measurements of relative humidity (RH) by using Graphene-Oxide/Polyvinyl-Alcohol (GO/PVA) composite. The microsensor is a mechanical oscillator composed of a proof mass with multilayer of nanomaterials (GO/PVA) and suspended by four crab leg springs. The realized redundant approach concerns the possibility to use different readout strategies in order to estimate the same measurand: RH. This is an intriguing solution to realize a “robust measurement system”, with multiple outputs by using the GO/PVA as functional material. In presence of RH variation: (1) it changes its mass and; as consequence; a variation of the natural frequency of the oscillator can be observed in the frequency domain; (2) it also varies the conductivity which can be measured by using two integrated electrodes. The sensor has been designed; studied; modeled and experimental results demonstrate the effectiveness of our implementation.

In this paper the possibility to exploit advantageously the Stochastic Resonance phenomenon in a Nonlinear Energy Harvester to scavenge energy from wide band mechanical vibrations is experimentally demonstrated. The device is demonstrated to be capable of scavenging energy in case of a subthreshold sinusoidal vibration and a wideband noise (limited at 100 Hz) superimposed. The existence of an optimal value of the noise intensity maximizing the switching ratio of the bistable beam, then the performances, is experimentally demonstrated. The harvester is observed to generate power up to about 60 µW and 150 µW in case of a subthreshold sinusoidal input at 1 Hz and 3 Hz with a superimposed noise limited at 100 Hz.

Among sensors using magnetic fluids, inclinometers are seriously addressed by the scientific community. This paper deals with an optical inclinometer exploiting a ferrofluid (FF) mass, a magnetic shaping system and an InfraRed (IR) readout strategy. A dedicated paradigm is also proposed, with the aim of defining a calibration model relating the inclinometer output quantity to the imposed tilt. The working range of the inclinometer is [−15°–15°]; the sensor accuracy and resolution are 0.61° and 0.005°, respectively. Moreover, the device shows a span-to-resolution ratio of 6 × 103.

This paper presents the realization and the experimental characterization of a low cost inkjet-printed DC magnetic field sensor. The device consists of a thin flexible cantilever beam operated at its first resonance frequency by the interaction between the target magnetic field and an AC current flowing through a coil printed on the beam. A resistive readout strategy is implemented by a printed strain gauge to measure the beam deflection strength, which is correlated to the target field strength. A dedicated electronics is used for the sake of the sensor operation. The sensor behaviour has been investigated for different values of the driving current and the target magnetic field. As an example, the sensor responsivity and resolution for a driving current of 20 mA @ 9.1 Hz (the beam resonance frequency) are 0.009 Ω/Ω/T and 1.2 mT, respectively.

A Nonlinear Energy Harvester designed to scavenge energy from wide-band mechanical vibrations is presented. The harvester exploits the advantages of a flexible beam in a Snap-Through-Buckling configuration and a novel magnetic repulsion mechanism to compensate for the asymmetric behavior of the device, in the vertical direction, due to the effect of the proof mass load. The device is demonstrated to be capable of scavenging energy in the range 0.5–10 Hz, which is compatible with many vibration sources, and to generate power up to 120 µW in case of an impulsive input at 5 Hz.