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This article presents an automated procedure for acquisition and analysis of BallistoCardioGraphy (BCG) traces. A triaxial accelerometer and a microcontroller unit are used to record heart-induced recoil forces generated from a lying subject. The problem of BCG J-peak annotation is split into two subtasks: candidates extraction, based on a detection signal, and actual annotation, guided by subject-specific search windows. Such a procedure is derived from an automatic calibration, which is carried out with no need of concurrent ElectroCardioGram (ECG) or user intervention. The algorithm also implements postannotation checks for refinement of annotation, which effectively reduces the number of missed J-peaks. The impact of each algorithm phase is analyzed, assessing statistical significance of each step; finally, performance is optimized in a data-driven fashion. Results show that the proposed methodology is able to achieve high sensitivity and precision (the median score is 98.9% and 98.1%, respectively) in J-peak detection. The quality of J-peaks timing annotation is further demonstrated by a very low discrepancy between BCG and ECG heart rate (HR) estimates. Overall population, the standard deviation of such error was found to be approximately 6.56 ms, whereas the mean absolute error was just 4.7 ms (i.e., ≈ 1.18; Ts, where Ts = 4 ms is the sampling period). Such scores, indeed, improve over recent related literature.

This article presents an unsupervised, automated procedure for the analysis of Seismocardiogram (SCG) signals. SCG is a measure of chest vibrations, induced by the mechanical activity of the heart, which allows extracting relevant parameters, including heart rate (HR) and HR variability (HRV). An initial self-calibration is performed, solely based on the SCG traces, yielding a suitable heartbeat template (personalized for each subject). Then, beat detection and timing annotation are performed in two steps: at first, candidate beats are identified and validated, by means of suitably defined detection signals; then, precise timing annotation is achieved by best aligning such candidate beats to the previously extracted template. The algorithm has been validated on two separate data sets, featuring different acquisition setups: the first one is the publicly available Combined measurement of ECG, Breathing and Seismocardiogram (CEBS) database, reporting SCG signals from the subjects lying in supine position, whereas the second one was acquired using a custom setup, involving the sitting subjects. Results show good sensitivity and precision scores (98.5% and 98.6% for the CEBS database, and 99.1% and 97.9% for the Custom one, respectively). In addition, comparison with electrocardiogram (ECG) gold-standard is given, showing good agreement between the beat-to-beat intervals computed from SCG and the ECG gold-standard: on average, $R^{2}$ scores of 99.3% and 98.4% are achieved on the CEBS and Custom data sets, respectively. Furthermore, a low rms error is achieved on the CEBS and Custom data sets, amounting to 4.6 and 6.2 ms, respectively (i.e., $2.3\;T_{s}$ and $3.1\;T_{s}$ , where $T_{s}$ is the sampling period): such results are well compared with related literature. Validation on two different data sets indicates the robustness of the proposed methodology.

This paper presents a fully automated procedure for acquiring and analyzing seismocardiographic (SCG) traces from an inertial measurement unit (IMU) placed over a subject’s sternum. An automated calibration procedure allows for straightforward adaption to different subjects. Calibration is performed once per subject, exploiting electrocardiogram (ECG) markers; relevant patterns and parameters are automatically extracted and used for successive SCG processing, which does not require concurrent ECG information any longer. Annotation of SCG traces is performed in two steps: in the first one, a suitably engineered signal is derived from SCG and used as coarse heartbeat detector; then, the annotation can be performed by comparing the prototype extracted at calibration time with segments of SCG data, near to the detected beats. The proposed methodology is validated by direct comparison with ECG, adopted as gold standard. In particular, three main metrics are taken into account: sensitivity (i.e., the percentage of correctly identified heartbeats, compared to ECG), precision (i.e., impact of false positives on truly detected beats), and $ {R^{2}}$ (i.e., linearity between beat-to-beat measurements as computed by ECG and SCG). Results show satisfactory performance, more than adequate to continuous, long-term monitoring: overall, approximately 90% of heartbeats are correctly detected, on average, with minimal false positives ( ${\approx 1}$ %). Linearity between ECG- and SCG-computed beat-to-beat intervals is extremely high ( $ {R^{2}>0.95}$ , on average), indicating good agreement between the two measurement methods. These results suggest that SCG can be used as a reliable, contactless measure of heart-related parameters.

Among sensible goals of active and assisted living paradigm is the unobtrusive monitoring of daily living activities. Based on such monitoring, anomalies and trends can be discovered, which possibly allows for early assessment of health issues and for prevention policies. However, when dealing with the home environment, and especially with older adults, obtrusiveness, usability, and cost concerns are of the utmost relevance. Smart objects can be designed to this purpose and deployed into the home: they usually feature low data rates and are customarily implemented by relying on conventional wireless sensor network approaches (ZigBee, Z-Wave, etc). This, however, results in “ad hoc” home networking, which is somehow obtrusive, complicated, and possibly expensive. In this paper, we discuss the implementation of behavioral sensors based on the familiar and ubiquitous Wi-Fi technology, suitable for a “plug-and-play” deployment. Sensors are connected to a cloud platform, embodying a genuine Internet of Things approach. With respect to conventional approaches, much better scalability, flexibility, and inexpensiveness can be attained. The main expected drawback comes from the higher power consumption, inherently needed to sustain much higher data rates. This paper focuses on such an issue, illustrating design techniques aimed at optimizing power consumption and battery lifetime. Performance results are shown, which definitely fall within a practical range and are fully comparable with more conventional approaches.

This paper presents an unsupervised methodology to analyze SeismoCardioGram (SCG) signals. Starting from raw accelerometric data, heartbeat complexes are extracted and annotated, using a two-step procedure. An unsupervised calibration procedure is added to better adapt to different user patterns. Results show that the performance scores achieved by the proposed methodology improve over related literature: on average, 98.5% sensitivity and 98.6% precision are achieved in beat detection, whereas RMS (Root Mean Square) error in heartbeat interval estimation is as low as 4.6 ms. This allows SCG heartbeat complexes to be reliably extracted. Then, the morphological information of such waveforms is further processed by means of a modular Convolutional Variational AutoEncoder network, aiming at extracting compressed, meaningful representation. After unsupervised training, the VAE network is able to recognize different signal morphologies, associating each user to its specific patterns with high accuracy, as indicated by specific performance metrics (including adjusted random and mutual information score, completeness, and homogeneity). Finally, a Linear Model is used to interpret the results of clustering in the learned latent space, highlighting the impact of different VAE architectural parameters (i.e., number of stacked convolutional units and dimension of latent space).

This paper presents an automated procedure for analyzing SeismoCardioGraphic (SCG) traces, i.e. chest vibrations induced by heart activity. Such signals are acquired by means of an inexpensive, compact accelerometer device, placed over the subject’s sternum and held in place by a light strap. The methodology for beat detection and annotation features a preliminary coarse beat detection phase, followed by a self-managed calibration, that is subsequently leveraged to carry out actual SCG annotation and beat localization. The performance of such process was measured and compared against the ECG gold standard, used only to provide ground-truth beat-to-beat intervals (i.e. R-peak landmarks). Results show high average values of sensitivity and precision (99.1% and 97.9%, respectively). The coefficient of determination R2 reaches a 0.984 value, which shows that the algorithm is able to temporally localize SCG complexes in a very consistent way, compared to the ECG gold standard; indeed, the variability between the ECG and SCG beat to beat measures is found to be very limited (σ diff ≈ 8.5 ms, i.e. less than one sample interval).

This paper introduces technical solutions devised to support the Deployment Site - Regione Emilia Romagna (DS-RER) of the ACTIVAGE project. The ACTIVAGE project aims at promoting IoT (Internet of Things)-based solutions for Active and Healthy ageing. DS-RER focuses on improving continuity of care for older adults (65+) suffering from aftereffects of a stroke event. A Wireless Sensor Kit based on Wi-Fi connectivity was suitably engineered and realized to monitor behavioral aspects, possibly relevant to health and wellbeing assessment. This includes bed/rests patterns, toilet usage, room presence and many others. Besides hardware design and validation, cloud-based analytics services are introduced, suitable for automatic extraction of relevant information (trends and anomalies) from raw sensor data streams. The approach is general and applicable to a wider range of use cases, however, for readability&rsquo, s sake, two simple cases are analyzed, related to bed and toilet usage patterns. In particular, a regression framework is introduced, suitable for detecting trends (long and short-term) and labeling anomalies. A methodology for assessing multi-modal daily behavioral profiles is introduced, based on unsupervised clustering techniques. The proposed framework has been successfully deployed at several real-users&rsquo, homes, allowing for its functional validation. Clinical effectiveness will be assessed instead through a Randomized Control Trial study, currently being carried out.

Artificial Intelligence in combination with the Internet of Medical Things enables remote healthcare services through networks of environmental and/or personal sensors. We present a remote healthcare service system which collects real-life data through an environmental sensor package, including binary motion, contact, pressure, and proximity sensors, installed at households of elderly people. Its aim is to keep the caregivers informed of subjects&rsquo, health-status progressive trajectory, and alert them of health-related anomalies to enable objective on-demand healthcare service delivery at scale. The system was deployed in 19 households inhabited by an elderly person with post-stroke condition in the Emilia&ndash, Romagna region in Italy, with maximal and median observation durations of 98 and 55 weeks. Among these households, 17 were multi-occupancy residences, while the other 2 housed elderly patients living alone. Subjects&rsquo, daily behavioral diaries were extracted and registered from raw sensor signals, using rule-based data pre-processing and unsupervised algorithms. Personal behavioral habits were identified and compared to typical patterns reported in behavioral science, as a quality-of-life indicator. We consider the activity patterns extracted across all users as a dictionary, and represent each patient&rsquo, s behavior as a &lsquo, Bag of Words&rsquo, based on which patients can be categorized into sub-groups for precision cohort treatment. Longitudinal trends of the behavioral progressive trajectory and sudden abnormalities of a patient were detected and reported to care providers. Due to the sparse sensor setting and the multi-occupancy living condition, the sleep profile was used as the main indicator in our system. Experimental results demonstrate the ability to report on subjects&rsquo, daily activity pattern in terms of sleep, outing, visiting, and health-status trajectories, as well as predicting/detecting 75% hospitalization sessions up to 11 days in advance. 65% of the alerts were confirmed to be semantically meaningful by the users. Furthermore, reduced social interaction (outing and visiting), and lower sleep quality could be observed during the COVID-19 lockdown period across the cohort.

This work presents a methodology to analyze and segment both seismocardiogram (SCG) and ballistocardiogram (BCG) signals in a unified fashion. An unsupervised approach is followed to extract a template of SCG/BCG heartbeats, which is then used to fine-tune temporal waveform annotation. Rigorous performance assessment is conducted in terms of sensitivity, precision, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of annotation. The methodology is tested on four independent datasets, covering different measurement setups and time resolutions. A wide application range is therefore explored, which better characterizes the robustness and generality of the method with respect to a single dataset. Overall, sensitivity and precision scores are uniform across all datasets ( p &gt, 0.05 from the Kruskal&ndash, Wallis test): the average sensitivity among datasets is 98.7%, with 98.2% precision. On the other hand, a slight yet significant difference in RMSE and MAE scores was found ( p &lt, 0.01 ) in favor of datasets with higher sampling frequency. The best RMSE scores for SCG and BCG are 4.5 and 4.8 ms, respectively, similarly, the best MAE scores are 3.3 and 3.6 ms. The results were compared to relevant recent literature and are found to improve both detection performance and temporal annotation errors.

This paper presents a methodology to assess heart-related parameters (such as heart rate and heart rate variability) from SeismoCardioGram (SCG) signals acquired with an Inertial Measurement Unit (IMU). Specific SCG landmarks are detected and identified, including Aortic valve Opening (AO) and Isovolumetric Moment (IM) phases. In order to improve detection accuracy and robustness, information from all 3 accelerometer’s axes are combined. Preliminary results are promising, highlighting good overall linearity ($r^{2} \gt$0.965) with respect to reference ECG-based (ElectroCardioGram) measurements. By considering all 3 accelerometer axes, performance improves over single-axis approaches. Results suggest the feasibility of accurate, continuous measurements of heart rate and its variability (in rest-conditions) in AAL scenarios, where such information may effectively complement physical activity and activity intensity data.

Environmental sensors are exploited in smart homes for many purposes. Sensor data inherently carries behavioral information, possibly useful to infer wellness and health-related insights in an indirect fashion. In order to exploit such features, however, powerful analytics are needed to convert raw sensor output into meaningful and accessible knowledge. In this paper, a complete monitoring architecture is presented, including home sensors and cloud-based back-end services. Unsupervised techniques for behavioral data analysis are presented, including: (i) regression and outlier detection models (also used as feature extractors for more complex models), (ii) statistical hypothesis testing frameworks for detecting changes in sensor-detected activities, and (iii) a clustering process, leveraging deep learning techniques, for extracting complex, multivariate patterns from daily sensor data. Such methods are discussed and evaluated on real-life data, collected within several EU-funded projects. Overall, the presented methods may prove very useful to build effective monitoring services, suitable for practical exploitation in caregiving activities, complementing conventional telemedicine techniques.

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.

Human monitoring is important in a wide range of applications and, most notably, in health management. Within the Active and Assisted Living (AAL) framework, human monitoring techniques have particular relevance: among AAL purposes is that of applying ambient intelligence paradigm to enable older adults or people with specific demands to live longer and independently in their homes, reducing the need of institutionalization. Sensors are used to track some features of daily living activities, which are then processed to infer health-relevant information. When dealing with the home environment, cost, intrusiveness and ease of installation and management are of the utmost importance: wireless sensors are therefore often used, and cloud-based service can be exploited to reduce the need for home-based hardware devices, this matching the increasingly widespread Internet of Things (IoT) paradigm. Wireless sensor connectivity may rely upon different standards and protocols: most frequently, WSN (wireless sensor network) dedicated protocols are exploited (e.g. ZigBee, Z-Wave) taking advantage of optimization toward low data-rate, low power features. However, ad-hoc deployment of such networks is required, which may result in expensive and burdening installation and management tasks. In this paper, we present a complete Wi-Fi based solution (named CARDEA Wi-Fi), in which sensors specifically designed for human monitoring purposes are straightforwardly connected to the internet cloud by exploiting the Wi-Fi communication protocol (universally diffused for home connectivity), without the need of local gateway devices (except for a Wi-Fi modem router). This results in lightweight installation and management procedures, leading to a “plug and play” approach, which is particularly appealing when interacting with persons having low or no technical skill. The CARDEA Wi-Fi architecture is discussed, and the implementation of a set of “behavioral” sensors is described. Preliminary figures about performance are also given, with particular reference to battery lifetime.

Continuous monitoring of daily life activities within the home environment is among most appealing and challenging goals of technologies supporting the Active and Assisted Living paradigm. The HELICOPTER (AAL-JP) project aims at merging conventional telemedicine and home monitoring into a unique, holistic framework. To this purpose, a network of heterogeneous sensors is deployed into the user's home: raw data coming from sensors should then be gathered and analyzed to extract meaningful information, possibly related to user's health and wellness. Besides the inherent complexity of designing and deploying a suitable infrastructure, intrusiveness, usability and acceptability concern come into play. In this paper, we introduce the overall project vision, focusing mostly on the infrastructure implementation and discussing the extension of the monitoring approach to a multi-user environment. In fact, if two or more persons live in the same environment, data coming from home sensors need disambiguation, i.e., the activity detected by a given sensor need to be ascribed to a specific user, in order to contribute to the appropriate behavioral profile. We propose a simple approach, based on fusion of data coming from wearable and home devices. We discuss lab tests, showing good accuracy in “action tagging”, and illustrate practical results coming from actual pilots, involving older end-users in different European countries.

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.

Recently, radio frequency identification (RFID) systems have gained popularity in manufacturing units, inventory, and logistics, as they represent an inexpensive and reliable solution for automatic identification. Moreover, RFID transponders are expected to become a key element in the ubiquitous computing scenario. Tags will likely be used to collect sensors data, enabling noninvasive environment monitoring. Low-cost passive UHF transponders are expected to play a major role in this context, due to extended read range capabilities. Within a passive tag, power harvested from the field irradiated by the reader during the communication should operate both digital control circuitry and potential sensing devices. Exploiting ultra-low power tag circuitry would provide sensing sections with higher energy, thus improving measurement performance. In this paper, the design of a novel circuit is presented, which implements the baseband processor of a UHF-RFID tag in compliance with the ISO 18000-6B protocol. Regardless of protocol selection issues, several power saving strategies are devised, both at the system and circuit levels, suitable for passive transponder implementation. Near-threshold operation has been exploited to attain ultra-low power consumption while keeping fair performance. A set of standard cells has been designed, suitable for the power-limited specific application. The proposed solution has been successfully checked by means of a physical implementation on CMOS 0.18 mum technology. Test chips have been characterized in terms of voltage and frequency operating range and power consumption figure has been extensively analyzed. Measurement results fully support the selected design approach: the baseband processor dissipates only 440 nW average power when operated at 800 kHz and 0.6 V. This extremely-low power consumption enables high-performance ubiquitous computing.

Continuous monitoring of safety and health conditions are among the primary goals of Ambient Assisted Living technologies. Effective solutions, aimed at fostering independent life of elderly persons, need for carefully balancing system intrusiveness, perspicacity, reliability and cost features. Heterogeneous networks, including different combination of environmental and personal wearable sensors can be used, with relevant value coming from data fusion: analysis techniques, aiming at inferring safety and health-related information in an indirect fashion from behavioral features are being deeply investigated with this aim. Device cooperation and interoperability are thus key factors: in this paper, the development of a wearable sensor suitable for broad range AAL application is introduced, addressing features specifically oriented to behavioral analysis. First, the device itself is capable of analyzing different features of the user motion patterns, synthesizing high-level information simple task identification, energy expenditure on board. This result in better battery management less data transferred over the radio link and interoperability thanks to data abstraction. Second, by means of a suitable operating protocol, it cooperates with environmental sensors e.g., a toilet sensor providing the latters with user identification information, and thus allowing to exploit related data even in a multi-user context. This avoid the need of more expensive and complex indoor localization techniques or of more intrusive identification technologies e.g., NFC/RFID tags.

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.

EU population is getting older, so that ICT-based solutions are expected to provide support in the challenges implied by the demographic change. At the University of Parma an AAL (Ambient Assisted Living) system, named CARDEA, has been developed. In this paper a new feature of the system is introduced, in which environmental and personal (i.e., wearable) sensors coexist, providing an accurate picture of the user's activity and needs. Environmental devices may greatly help in performing activity recognition and behavioral analysis tasks. However, in a multi-user environment, this implies the need of attributing environmental sensors outcome to a specific user, i.e., identifying the user when he performs a task detected by an environmental device. We implemented such an "action tagging" feature, based on information fusion, within the CARDEA environment, as an inexpensive, alternative solution to the problematic issue of indoor locationing.

World population is facing deep demographic changes. A number of societal challenges are to be tackled, and possible support from ICT is sought for. AAL (Ambient Assisted Living) technologies, aimed at fostering independent life of elderly people, are therefore becoming increasingly important. Based on the AAL-system named CARDEA and previously developed at the University of Parma, in this paper a new feature is presented, named CARDEAGate and aimed at providing an inexpensive and scarcely intrusive way for providing user localization and identification information. Such information is needed to implement a number of useful functionalities, and most notably to carry out behavioral analysis in a multi-user context. A simple approach, based on a “wireless barrier” and exploiting ZigBee protocol features is shown to provide reliable monitoring information. Preliminary test results are given, whereas a full characterization is currently under way.

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 Potential (SSVEP) is a good candidate for BCI-enabled communication/control applications. In this paper, we compare different reference signal processing methods, including two we developed ad hoc, assessing how they perform with respect to different indicators (not necessarily convergent, such as accuracy, computational effort and responsiveness). All the tests are performed on the subject population as a whole, in an effort to produce subject-independent methods. We also discuss a strategy for improving the classification accuracy by introducing an indicator related to the prediction confidence. Finally, a method for adaptively changing the length of the observed EEG window is presented.

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.

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 this paper, the infrastructure supporting the HELICOPTER AAL-JP project is described. The project aims at introducing behavioral analysis features for early detection of age-related diseases: to this purpose, a heterogeneous sensor network has been designed and implemented, encompassing in the same vision environmental, wearable and clinical sensors. In order to make environmental sensors suitable for behavioral inference, the issue of activity tagging i.e., attribution to a given user of the action detected by the sensors needs to be tackled. Within the HELICOPTER scenario, cooperation between environmental and wearable sensors is exploited to this aim. Preliminary results offer encouraging perspectives: piloting phase, which will validate the approach on a larger scale, is close to start.

Brain Computer Interface (BCI) technology is an alternative/augmentative communication channel, based on the interpretation of the user’s brain activity, who can then interact with the environment without relying on neuromuscular pathways. Such technologies can act as alternative HCI devices towards AAL (Ambient Assisted Living) systems, thus opening their services to people for whom interacting with conventional interfaces could be troublesome, or even not viable. We present here a complete solution for BCI-enabled home automation. The implemented solution is, nonetheless, more general in the approach, since both the realized hardware module and the software infrastructure can handle general bio-potentials. We demonstrate the effectiveness of the solution by restricting the focus to a SSVEP-based, self-paced BCI, featuring calibration-less operation and a subject-independent, “plugp at the same time, the signal processing chain will be presented, introducing a novel method for improving accuracy and immunity to false positives. The results achieved, especially in terms of false positive rate containment (0.26 min−1) significantly improve over the literature.

A balanced diet is the key to a healthy lifestyle and is crucial for preventing or dealing with many chronic diseases such as diabetes and obesity. Therefore, monitoring diet can be an effective way of improving people’s health. However, manual reporting of food intake has been shown to be inaccurate and often impractical. This paper presents a new approach to food intake quantity estimation using image-based modeling. The modeling method consists of three steps: firstly, a short video of the food is taken by the user’s smartphone. From such a video, six frames are selected based on the pictures’ viewpoints as determined by the smartphone’s orientation sensors. Secondly, the user marks one of the frames to seed an interactive segmentation algorithm. Segmentation is based on a Gaussian Mixture Model alongside the graph-cut algorithm. Finally, a customized image-based modeling algorithm generates a point-cloud to model the food. At the same time, a stochastic object-detection method locates a checkerboard used as size/ground reference. The modeling algorithm is optimized such that the use of six input images still results in an acceptable computation cost. In our evaluation procedure, we achieved an average accuracy of 92 % on a test set that includes images of different kinds of pasta and bread, with an average processing time of about 23 s.

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.

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.

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.

The European population is becoming older and older, causing AT (Assistive Technology) and AAL (Ambient Assisted Living) topics to become increasingly important. A ZigBee based low-cost home automation system named CARDEA has been developed at the University of Parma, with the aim to allow elderly people to live their lives autonomously and independently. In this paper a new feature is presented, named CARDEAGate: 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 is very useful to supervise the habits of a not completely self-sufficient person monitoring the access to particular locations or tracking he/she in order to execute a long term behavioral analysis.

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.

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.