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Food intake and eating habits have a significant impact on people’s health. Widespread diseases, such as diabetes and obesity, are directly related to eating habits. Therefore, monitoring diet can be a substantial base for developing methods and services to promote healthy lifestyle and improve personal and national health economy. Studies have demonstrated that manual reporting of food intake is inaccurate and often impractical. Thus, several methods have been proposed to automate the process. This article reviews the most relevant and recent researches on automatic diet monitoring, discussing their strengths and weaknesses. In particular, the article reviews two approaches to this problem, accounting for most of the work in the area. The first approach is based on image analysis and aims at extracting information about food content automatically from food images. The second one relies on wearable sensors and has the detection of eating behaviours as its main goal.

The ActivAge project is currently exploring heterogeneous IoT solutions for AHA over 9 different European Deployment Sites (DS), in an interoperable network. In this work, we present main features of the DS-RER experimentation, being implemented in the Emilia-Romagna Region (RER, Italy) and mostly aimed at effectively embedding IoT-enabled solutions within the actual organization and practices of regional social and health-care services, to support users over 65 suffering post-stroke conditions.

Healthcare paradigms, due to demographic changes, are definitely aiming at effective prevention and early diagnosis strategies. This inherently calls for continuous monitoring of (ageing) people in their own living environment and while attending at daily living activities. Such monitoring may rely on a wide range of sensing technologies, each featuring different trade-offs among main parameters such as accuracy, expressivity, cost, reliability and intrusively. This includes clinical sensors (suitable for self-managed, precise measurement of physiological parameters), wearable devices (continuously monitoring health or activity features) and environmental sensors distributed in the living environment (suitable for indirect assessment of relevant behaviours, besides serving basic safety purposes). In this chapter, the meaning of human monitoring from a home-care point of view will be defined, and the basic sensor categories will be reviewed. Then, the design and the main features of the CARDEA home monitoring system will be discussed. Finally, some application examples, coming from European project living-lab experiences, will be illustrated, and some results obtained by data fusion and analysis techniques, suitable for inferring health and wellness information by effectively correlating raw data coming from the sensor field, will be presented.

We evaluated the effectiveness in classifying food images of a deep-learning approach based on the specifications of Google's image recognition architecture Inception. The architecture is a deep convolutional neural network (DCNN) having a depth of 54 layers. In this study, we fine-tuned this architecture for classifying food images from three well-known food image datasets: ETH Food-101, UEC FOOD 100, and UEC FOOD 256. On these datasets we achieved, respectively, 88.28%, 81.45%, and 76.17% as top-1 accuracy and 96.88%, 97.27%, and 92.58% as top-5 accuracy. To the best of our knowledge, these results significantly improve the best published results obtained on the same datasets, while requiring less computation power, since the number of parameters and the computational complexity are much smaller than the competitors?. Because of this, even if it is still rather large, the deep network based on this architecture appears to be at least closer to the requirements for mobile systems.

Brain-Computer Interface (BCI) can provide users with an alternative/augmentative interaction path, based on the interpretation of their brain activity. Steady State Visual Evoked Potentials (SSVEP) paradigm has many appealing features, aiming at implementing BCI-enabled communication-control applications. In this paper, we present a complete signal processing chain for a self-paced, SSVEP-based BCI. The proposed approach mostly focuses at reducing the user effort in dealing with BCI, featuring no need of user-specific calibration or training. In this paper, the classification algorithm is introduced and first validated on offline waveforms, aiming at improving classification accuracy and minimizing the false positive rate. Then, implementation of an online, self-paced SSVEP BCI is illustrated. The scheme refers to a four-way choice and exploits discrimination between intentional control states and nocontrol ones. Good performance is achieved, both in terms of true positive rate (94%), as well as low false positive rate (0.26 min(-1)), even in experiments carried out outside lab-controlled conditions.

An active pixel sensor has been developed using standard CMOS technology, UMC 0.18 μ m with no epitaxial layer, with pixel size 4.4 × 4.4 μ m , in the framework of the INFN RAPS project. In this work we will report on the results obtained using several types of ionizing radiation sources (laser, X-ray tubes, β and γ ) to test extensively the device. Some of the main results obtained are: a signal/noise value for minimum ionizing particles of about 20, a very good linearity of the response, a good spatial confinement of the signal (cluster size of the order of few pixels).

Brain–Computer Interfaces (BCI) rely on the interpretation of brain activity to provide people with disabilities with an alternative/augmentative interaction path. In light of this, BCI could be considered as enabling technology in many fields, including Active and Assisted Living (AAL) systems control. Interaction barriers could be removed indeed, enabling user with severe motor impairments to gain control over a wide range of AAL features. In this paper, a cost-effective BCI solution, targeted (but not limited) to AAL system control is presented. A custom hardware module is briefly reviewed, while signal processing techniques are covered in more depth. Steady-state visual evoked potentials (SSVEP) are exploited in this work as operating BCI protocol. In contrast with most common SSVEP-BCI approaches, we propose the definition of a prediction confidence indicator, which is shown to improve overall classification accuracy. The confidence indicator is derived without any subject-specific approach and is stable across users: it can thus be defined once and then shared between different persons. This allows some kind of Plug&Play interaction. Furthermore, by modelling rest/idle periods with the confidence indicator, it is possible to detect active control periods and separate them from “background activity”: this is capital for real-time, self-paced operation. Finally, the indicator also allows to dynamically choose the most appropriate observation window length, improving system’s responsiveness and user’s comfort. Good results are achieved under such operating conditions, achieving, for instance, a false positive rate of 0.16 min−1, which outperform current literature findings.

We present a complete BCI-enabled (Brain Computer Interface) solution for Ambient Assisted Living system control. BCI are alternative, augmentative communication means capable of exploiting just the brain waveforms to infer intent, thus potentially posing as a technological bridge capable of overcoming limitations in the usual neuromuscular pathways. The module was completely developed in a customized way, encompassing hardware and software components. We demonstrate the effectiveness of the approach on a practical control scenario in which the user can issue 4 different commands, at his own pace and will, in real-time. No initial calibration is necessary, in line with the aimed plugamp;play approach. Results are very promising, especially in false positives rejection, well improving over literature.

Behavioral analysis, based on unobtrusive monitoring through environmental sensors, is expected to increase health awareness of AAL systems. In this paper, techniques for assessing behavioral quantitative features are discussed, suitable for detecting behavioral anomalies in an unsupervised fashion, i.e., with no need of defining target reference behaviors and of tuning user-specific threshold parameters. Such technique is being exploited for analyzing data coming from a set of European pilot sites, in the framework of the EU/AAL-JP project "FOOD", specifically focused at kitchen activity. Simple results are illustrated, suitable for proof-of-concept validation.

As the average age of the EU population increases, ICT solutions are going to play a key role in order to find answers to the new challenges the demographic change is carrying on. At the University of Parma an AAL (Ambient Assisted Living) system named CARDEA has been developed during the last 10 years. Within CARDEA, behavioral analysis is carried out, based on environmental sensors. If multiple users live in the same environment, however, data coming from sensors need to be properly tagged: in this paper, a simple technique for such tagging is proposed, which exploits the same wireless transmission used for transmitting data, thus not requiring additional hardware components and avoiding more complex and expensive (radio)localization techniques. Preliminary results are shown, featuring a satisfactory accuracy.

Gate currents are becoming a major concern for ULSI circuit designers. A standard circuit design flow that takes into account such effects is needed in order to take advantage from nanometer-sized devices performance. Some suggestions on how to approach permeable-gate-device circuit simulation are proposed in this paper, focusing on gate-current-related effects on circuit performance. A simple, yet complete, standard CMOS circuit has been designed and its functional and performance indices have been evaluated, depending on the actual oxide thickness. To correlate performance directly to the fabrication parameters, a mixed-mode approach using both physical and circuit simulations has been adopted. Model parameters were calibrated on actual measurements. Simulation results have been compared for ideal- and permeable-gate devices. Although circuit functionality is not affected (within the considered technology range, at least), significant performance alterations are highlighted.

Device simulation allows for accurate analysis of device behavior, accounting for several physical details that cannot easily be taken into account within compact, equivalent-circuit models. This is especially true for some issues typical of the design of silicon radiation detectors, where silicon properties are exploited in a non-conventional way and radiation damage raises severe reliability concerns. In this paper, a couple of significant applications of device simulation to the investigation and design of advanced solid-state radiation sensors are presented. More specifically, (i) radiation damage influence on detectors operating at cryogenic temperatures is successfully modeled and (ii) features of an innovative scheme for CMOS active pixel sensors are analyzed by means of mixed-mode simulation tools. From these examples, the usefulness and potentiality of advanced simulation techniques in the perspective of radiation detectors can be appreciated.

A Brain-Computer Interface (BCI) is an alternative/augmentative communication device that can provide users with an interaction path, based on the interpretation of his/her brain activity. Such technology, applied to AAL contexts, could potentially have a major impact on daily-living, extending the ageing at home paradigm also to users with severe motor impairments, for whom the interaction with the surrounding environment is troublesome. In this paper, a low cost BCI development platform is presented and its performance assessed by means of an illustrative application example using SSVEP paradigm to switch on and off lights. Comparison against other SSVEP signal processing methods in literature is also made.

In this paper a wearable multi-sensor device is used for a Behavioral Analysis (BA) focused on Human Activity Recognition (HAR) and Indoor Localization (IL). The analysis exploit a wearable device equipped with inertial sensors like accelerometer, gyroscope and compass in order to evaluate quantity and quality of movements.

In recent years, sophisticated technologies for personal monitoring are rapidly spreading. Mainly, these devices are medical sensors for domestic use, that allow for monitoring the most important physiological parameters. These smart devices enable to detect information closely related to the user’s health. Despite the effectiveness of these tools, such approaches do not allow for the detection of the personal wellness state, in a wider sense. From this point of view, more information can be detected by the analysis of user’s behavior. Especially in the case of elderly users, changes of behavior may be clues of situation of uneasiness or worsening of health condition. To support the monitoring of wellness conditions, tools and techniques for behavioral analysis have been developed at Information Engineering Department of University of Parma (ITALY). In this paper, a non-invasive and cost-effective technology is presented: the CARDEAdomus Ambient Assisted Living System. Eventually, results related to a experimentation, carried out by means of the System in a real context, are showed.

The European population is becoming older and older, causing AT and AAL topics to become more and more important. A ZigBee based low-cost home automation system named CARDEA has been developed at the University of Parma, with the aim to permit elderly people to live their lives autonomously and independently. In this paper is presented a new feature: a gateway monitoring system which allows to detect crossing of a doorway or a predefined gateway and, if the person is carrying a wearable ZigBee sensor, to identify he/she. This technology could be helpful to control movements of a not completely self-sufficient person, to supervise the access to a particular location or to keep track of the person’s habits to possibly relate them to his/her health state.

The CMS experiment at the LHC will comprise a large silicon strip tracker. This article highlights some of the results obtained in the R&D studies for the optimization of its silicon sensors. Measurements of the capacitances and of the high voltage stability of the devices are presented before and after irradiation to the dose expected after the full lifetime of the tracker.

The adoption of overhanging-metal contacts has been suggested as an effective means to limit breakdown risks in heavy-damaged high-voltage biased microstrip detectors. In this paper, the influence of the overhang on device noise parameters is analyzed, with particular reference to the interstrip capacitance. Data have been collected on a set of detectors featuring variable overhang extensions and different width/pitch ratios, and numerical simulation has been exploited to provide physical interpretation of the experimental findings. In particular, the nontrivial dependence of interstrip capacitance over geometrical parameters is discussed. By looking at leakage currents and charge collection as well, it is shown that limited-extension overhangs still have highly beneficial effects on the breakdown properties while having no practical drawbacks on the detector performance.

In this paper, a computer-based analysis of AC-coupled silicon microstrip detectors is presented. The study aims at investigating the main geometrical parameters responsible for potentially critical effects, such as early micro-discharges and breakdown phenomena. The adoption of CAD tools allows for evaluating the actual field distribution within the device, and makes it possible to identify critical regions. The adoption of overhanging metal strips is shown to have a positive impact on the electric field distribution, reducing corner effects and thus minimizing breakdown risks.

Interstrip and backplane capacitances on silicon microstrip detectors with p+ strip on n substrate of 320 μm thickness were measured for pitches between 60 and 240 μm and width over pitch ratios between 0.13 and 0.5. Parametrisations of capacitance w.r.t. pitch and width were compared with data. The detectors were measured before and after being irradiated to a fluence of 4×10 14 protons / cm 2 of 24 GeV /c momentum. The effect of the crystal orientation of the silicon has been found to have a relevant influence on the surface radiation damage, favouring the choice of a 〈1 0 0〉 substrate. Working at high bias (up to 500 V in CMS) might be critical for the stability of detector, for a small width over pitch ratio. The influence of having a metal strip larger than the p+ implant has been studied and found to enhance the stability.

The changes of the electrical properties induced by hadron irradiation on silicon detectors have been studied by using the device level simulator HFIELDS. The model of the radiation damage assumes the introduction of radiation-induced acceptor and donor “deep-levels”. The electric field profile and the space charge region extension have been calculated for differently irradiated structures. The simulation has been carried out at different biases in order to study the evolution of the space charge region of irradiated detectors as a function of the applied voltages, below and above the full depletion. The time-dependent current responses and the charge collection properties of the structure illuminated by a red LED light have been calculated. The use of the red light results in a shallow (quasi-surface) generation of e–h pairs in silicon, which has been properly taken into account by the simulation. The results of the simulations have been compared to experimental measurements carried out at CERN on samples irradiated with 24 GeV/c protons. The comparison results in a satisfactory agreement, and supports the physical interpretation of experimental data.

In this paper, numerical analysis techniques are applied to the study of microstrip silicon detectors exploited in the field of high-energy physics. At high luminosity required by future experiments, radiation hardness of such device becomes a critical issue. The adoption of relatively low-resistivity substrates has been suggested as a key to face such a problem: simulations have been carried out to verify this assumption. Comparisons have been made in terms of depletion voltage, as well as of charge-collection efficiency, by exploiting some of the features of a customized simulation environment. Estimated, long-term radiation hardness of low-resistivity detectors favorably compares with high-resistivity ones.

For the construction of the silicon microstrip detectors for the Tracker of the CMS experiment, two different substrate choices were investigated: A high-resistivity (6 k cm) substrate with (111) crystalorientation and a low-resistivity (2k cm) one with (100) crystalorientation. The interstrip and backplane capacitances were measured before and after the exposure to radiation in a range of strip pitches from 60 μm to 240 μm and for values of the width-over-pitch ratio between 0.1 and 0.5.

In this paper, a simulator based on a global electromagnetic model is presented, suited for the analysis of HF integrated and hybrid electronic circuits. The model is based on the self-consistent solution of Maxwell's equation and of semiconductor transport equations, exploiting a generalized finite-difference time-domain (FDTD) scheme. The tool is, therefore, capable of accounting, on a distributed basis, for actual interactions between wave propagation and charge transport, anti is capable of providing a physically based picture of traveling-wave semiconductor devices. The implementation is such that more conventional algorithms (e.g. lumped-element FDTD or plain FDTD) can be regarded as a subset of the global scheme itself. This makes it possible to intermix different physical models, featuring different degrees of physical accuracy and computational efficiency, within the same simulation environment. Main features of such an environment are described by means of the simulation of a simple 76-GHz distributed switch.

The numerical simulation of a silicon microstrip detector is discussed. Physical models for the bulk radiation damage have been taken into account, based on a generalized Shockley–Read–Hall expression of the recombination rate. The actual shape of depletion layer, depending on the radiation fluence, has been investigated. The build-up of a dual depletion layer, as reported in some literature works, has been described and interpreted.

A large use of silicon microstrip detectors is foreseen for the intermediate part of the CMS tracker. A specific research and development program has been carried out with the aim of finding design layouts and technological solutions for allowing silicon microstrip detectors to be reliably used on a high radiation level environment. As a result of this work single sided, AC-coupled, polysilicon biased, 300 μ m thick, p + on n substrate detectors were chosen. Irradiation tests have been performed on prototypes up to fluence 2×10 14 n/cm 2 . The detector performances do not significantly change if the detectors are biased well above the depletion voltage. S / N is reduced by less than 20%, still enough to insure a good efficiency and space resolution. Multiguard structures has been developed in order to reach high voltage operation (above 500 V).

A Brain-Computer Interface (BCI) is an alternative/augmentative communication device that can provide users (for example, individuals lacking voluntary muscle control) with an interaction path, based on the interpretation of his/her brain activity. In this paper, the design and implementation of a flexible, low-cost BCI development platform is presented; this platform could serve as a workbench to develop compact, standalone BCI embedded modules, specifically targeted to (even if not limited to) AAL control purposes. First, a low-cost, custom, bio-potential acquisition unit was realized; then, a Matlab-based environment was developed for EEG (ElectroEncephaloGram) signal analysis and processing. An application example involving a 4-class SSVEP-based BCI is presented, along with a novel classification algorithm which achieved 94.7 % classification accuracy.

Kitchen activities involve complex and articulate interactions with heterogeneous technologies and devices. In this paper, outcomes of the FOOD AAL-JP project are presented, related to the development of a kitchen environment implementing ambient-assisted-living features, aimed at increasing safety, autonomy, engagement and reward in dealing with food-related activities. © 2014 Springer International Publishing.

This paper describes the MyCmon Cloud-Based Smart Home Monitoring System including a home automation system permanently connected through the internet to a Cloud-based control system. MyCmon aims at supporting independent living of elderly people and people with disabilities, by making monitoring functionalities available to their loved ones or to caregivers, anywhere and at any time, through mobile devices. The project is under development, expecting to be ready for the market by March 2014.

MuSA (Multi Sensor Assistant) is a wearable multi-sensor device designed for elderly people monitoring. The system features healthcare services equipping a fall detector, a user alarm button, heartbeat, breathing rate and body temperature evaluation. By integrating MuSA in an ambient-assisted living framework, CARDEA, data fusion approaches can be implemented to obtain a behavioral profile which can be significant for the caregivers. The objective is to provide a set of features that integrated all together may foster a safe, independent and autonomous life to elderly in their home in accordance to the AAL (Ambient Assisted Living) paradigm. This paper describes the main concepts of MuSA and the details of the single functionalities. The suitable low-cost approach and the adequate quality of the system response, at the same time, have been proved by field tests of the device.

In this work, the application of numerical device simulation to the analysis of high resistivity silicon microstrip detectors is illustrated. The analysis of DC, AC and transient responses of a single-sided, DC-coupled detector has been carried out, providing results in good agreement with experimental data. In particular, transient - mode simulation has been exploited to investigate the collection of charges generated by ionizing particles. To this purpose, an additional generation term has been incorporated into the transport equations: the motion of impact-generated carriers under the combined action of ohmic and diffusive forces is hence accounted for. Application to radiation tolerances studies is also introduced

In this work, the application of the general-purpose device simulator HFIELDS to the analysis of silicon microstrip detectors is presented. In the framework of CMS collaboration, a comprehensive device characterization has been performed by means of steady-state (DC) and small-signals (AC) numerical analyses. The study of charge collection dynamics has been carried out as well, by means of transient analysis. Simulation results exhibit a good agreement with literature data, and allow for detailed insights of device behavior. This makes it possible to investigate device-performance sensitivity to fabrication and environmental parameters and highlights potential applications of numerical analysis as a device design and optimization aid.

In this paper, the extension of numerical simulation techniques to the analysis of light-addressable potentiometric sensors (LAPS) is discussed in detail. To this purpose, proper physical models of both the ion-sensitive and the photo-sensitive transduction mechanisms have been incorporated into the framework of a general-purpose device simulator. A self-consistent, accurate picture of charge transport within the device under the combined action of electrolyte ion layers and of luminous stimulus is recovered, which in turn allows for detailed analysis of the device behavior and for fine-tuning of the fabrication process. Extensive comparison with actual LAPS measurement has been performed, validating the tool and illustrating its flexibility and application range.

In this paper, a simulation technique is introduced, which couples solid-state device modeling and full-wave, electromagnetic simulation of interconnections. A three-dimensional (3-D) FDTD scheme is adopted to describe the circuit passive part, whereas numerical device simulation techniques are employed for the active semiconductor devices. The resulting scheme allows for accurate mixed-mode simulation, which inherently accounts for propagation and radiative effects. An application example is discussed, consisting of the simulation of a Si-MMIC RF switch; results have been compared with predictions coming from a standard microwave circuit simulator, validating the tool, and illustrating its application range.

Transport properties of the silicon crystal are sensitive, to some extent, to mechanical perturbations: this allows for integrating mechanical sensors, together with the sensing circuitry, within a silicon chip by using an almost standard IC technology. In this paper, the numerical simulation of a silicon pressure sensor, based on the piezoresistive effect, is described. The simulated transducer is made of a thin silicon diaphragm, on top of which a four-resistor bridge is diffused. For a given pressure, the distribution of the stress components over the diaphragm is feeded to the program, which then computes the sensor response depending on its geometrical and physical features. To this purpose, an anisotropical, stress-dependent, mobility model has been introduced into the device simulator HFIELDS-3D.

This paper presents a detailed analysis of Wr@p1 technology (Web Ready Appliances Protocol, formerly Power Modulation), an ultra-low-cost powerline communication solution conceived for the electrical appliance market. Wr@p technology aims at making household appliances capable of network communication without significantly affecting their industrial cost. This may foster the diffusion of “smart” appliances and enables their interaction with the power-distribution grid, thus contributing to the smart grid paradigm. The Wr@p transceiver establishes a narrow-band communication channel exploiting the “last meter”, i.e. the appliance power-supply cord; at the outlet, a proxy device, the smart adapter (SA), flexibly deals with standard home networking protocols. At the appliance side, such an approach (i) allows for connectivity at negligible cost and, (ii) keeps hardware and software virtually independent of the actual home networking protocol (since different configurations of the smart adapter take care of it). In this work, after recalling the basics of Wr@p point-to-point communication, an extension to the multipoint-to-point scenario is introduced. Design of silicon integrated implementations into commercial microcontroller architectures are presented and the results of extensive test of fabricated devices are illustrated. Moreover, a complete Wr@p development solution is presented, featuring wireless networks integration.