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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 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.

Location-based services have increased in popularity in recent years and can be fruitfully exploited in the field of smart homes, opening the doors to a wide range of personalized services. In this context, radio technology can be widely employed since, other than connecting devices in the home system, it offers solutions for the user localization issue without the need of any extra device. Techniques based on received signal strength indicator (RSSI) are often used, relying on fingerprinting or proximity algorithms. In this paper, a novel RSSI-based fingerprinting approach for room-level localization is presented: it is a threshold algorithm based on receiver operating characteristic analysis. Moreover, the actual user location is estimated from his/her interaction with the home system devices deployed in the house: if the home environment is inhabited by more than one person, it becomes of utmost importance the identification of who is actually interacting with a given device. A proximity method is exploited for this purpose. Tests have been carried out to characterize the approach, particularly, the effects of RSSI samples, number and position, of the anchor nodes have been analyzed. Finally, some considerations about power consumption of the mobile node have been presented.

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.

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.

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.

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.

This paper focuses at features introduced in the wearable sensor MuSA, to support behavioral analysis within the context of the HELICOPTER project, funded in the AAL European joint program. In particular, the wearable device performs two key function: on one hand it is used as a behavioral data source, continuously monitoring the quantity of user physical activity (through the energy expenditure index evaluation), location and posture; on the other hand, MuSA enables fusion of data coming from the environmental sensors, properly attributing actions on a particular sensor to a specific user in a multi-user environment. These function are carried out without the need of external devices (RFID tags etc.), but only relying on sensors embedded on the wearable device and its communication capabilities. Some sample results coming from pilot studies are shown.

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) 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. A complete BCI implementation is presented and discussed, briefly introducing the customized hardware and focusing more on the signal processing aspects. The BCI is based on SSVEP signals, featuring self-paced calibration-less operation, aiming at a “plug&play” approach. The signal processing chain is presented, introducing a novel method for improving accuracy and immunity to false positives. The results achieved, especially in terms of false positive rate containment (0.16 min−1) significantly improve over the literature. In addition, a possible integration of EMG signals in a hybrid-BCI scheme is discussed, serving as a binary switch to turn on/off the EEG-based BCI section (and the flashing stimuli unit). This can have positive impact on both the user’s comfort as well as on the resilience towards false positives. Preliminary results for jaw clench recognition show good detectability, proving that such integration can be implemented.

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.

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. BCI can thus be placed in the context of human-machine interfaces and, considering a possible application scenario to smart homes, they can serve as a technological bridge to make Active and Assisted Living AAL systems' functionalities accessible to subjects who would not otherwise be able to actively use. In this paper, BCI is specifically conceived for AAL system control experience, developed ad-hoc, considering cost and compactness constraints, besides classification performance. The implemented solution is quite general, as it can handle multiple bio-potentials: this feature allows to exploit different information channels, namely, ElectroEncephaloGraphy EEG and ElectroMyoGraphy EMG. Each subsystem EEG, EMG is presented and its performance discussed; both can operate in real-time and in self-paced mode i.e. they automatically recognize if a command is being issued and, in this case, which one. In particular, the EEG part, based on Steady State Visual-Evoked Potentials SSVEP, can achieve very good results in terms of false positive rejection, improving over the state of the art False Positive Rate: 0.16i¾źmin-1. Moreover, such results are achieved without any initial system calibration phase. Meanwhile, the EMG subsystem can be used as a smart switch for SSVEP stimuli control on/off, improving user's comfort when no control periods are desired.

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 communicationcontrol applications.

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.

The possibility to develop compact, cost effective Brain-Computer Interface (BCI) solutions could take another step into transferring and spreading such technologies outside the labs. We present here our compact EEG hardware unit, and compare its performance against a commercial device. Then, we will demonstrate its use in a SSVEP-based BCI. The signal processing chain is briefly discussed. We also present a strategy for improving classification accuracy and false positives immunity by introducing an indicator related to the prediction confidence. A method for adaptively changing the length of the observed EEG window is also discussed. All these ideas are tested in an online, self-paced 4 class SSVEP-based BCI Moreover, tests are performed on the subject population as a whole, in an effort to produce subject-independent methods. Good performance is achieved, both in terms of true positive rate (>94%), as well as low false positive rate (0.26 min−1)

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.

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.

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.

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.

A Brain-Computer Interface (BCI) is an alternative/augmentative communication device that can provide users with a different interaction path, based on the interpretation of his/her brain activity. Such technology, applied to Ambient Assisted Living (AAL) contexts, could potentially make the full set of features of such systems accessible to users affected by severe motor impairments, for whom the interaction with the surrounding environment is troublesome. In this paper, a low cost BCI development platform, consisting of a hardware acquisition unit and a Matlab-based prototyping environment is presented. BCI performance assessed by means of an illustrative application example using a 4 class SSVEP paradigm to switch on and off lights. Comparison with other reference methods from literature is also presented.

The design of an Ambient Assisted Living (AAL) aims to create better living conditions for the elderly, especially those who choose to live in their own houses, as long as possible. To this objective, AAL systems must mainly monitor the health status of the elderly through the analysis of data gathered via technologies based on sensor devices. Sensors networks produce collections of data of fine-grained nature, regarding general information such as device name, data type, data value, timestamp, but also specific one. The data analysis, due to its granularity and heterogeneity, makes very difficult to infer a clear overall view of the status of the elderly, it demands automatic tools for selecting meaningful data and mapping them in a common conceptual schema. In the last decade, ontologies became widely used tool to describe application domains and to enrich data with its meaning. In this paper, we propose an ontology-based methodology to perform semantic queries on a data repository, where records originated from networks of heterogeneous sources are stored. A semantic query is a pattern matching process that supports the recognition of specific temporal sequences of events that can be extracted from fine-grained data. In our framework a domain ontology are exploited at different levels of abstraction and the reasoning techniques are used to pre-process data for the final temporal analysis. The proposed approach is a deliverable of the ongoing AALISABETH project funded by Region Marche Government; while the software component is integrated into the AALISABETH framework.

Brain Computer Interfaces (BCI) can provide severely impaired users with alternative communication paths, by means of interpretation of the user's brain activity. Among BCI operating paradigms, SSVEP is largely exploited for its potentially high throughput and reliability. In this paper, two novel SSVEP processing algorithms are presented, focused on calibration-free operation and computational efficiency, targeted for development of BCI embedded modules. A comparison with other popular SSVEP signal processing algorithm (MEC, AMCC, CCA) is also made; results demonstrate the feasibility and effectiveness of the proposed solutions.

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.

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.

Brain Computer Interface (BCI) technology can provide users lacking voluntary muscle control with an augmentative communication channel, based on the interpretation of her/his brain activity. Such technologies, combined with AAL (Ambient Assisted Living) systems, can potentially have a great impact on daily living, extending the scope of the ageing at home paradigm also to individuals affected by severe motor impairments, for whom interacting with the environment is troublesome. In this paper, a low cost BCI development platform is presented; it consists of a customized EEG acquisition unit and a Matlab-based signal processing environment. An application example using SSVEP paradigm is discussed.

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.

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.

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.

A 3-D general-purpose device simulator is presented, and issues related to its geometrical flexibility and computational efficiency are discussed. Triangular-based prismatic elements are used as building blocks for the discretization of device equations, providing a reasonable tradeoff between simplicity and geometrical flexibility. Suitable numerical techniques, including a 'local-solution' algorithm, have been implemented in order to improve the computational efficiency of the code. A general treatment has been devised to handle floating-gate devices, which allows the program to cope with complex structures, such as a dual EPROM structure. Three-dimensional and two-dimensional simulation results are compared. >

In this paper, the development of a prototypal wireless sensor platform is described, aimed at assisting elderly people and people with disabilities in their daily living activities at home. The wireless sensor network is embedded into a more general home control and monitoring network, from which it can borrow remote communication and supervision facilities, enhancing versatility and reliability. A wearable sensor has been developed, capable of smart recognition of abnormal gait and falls. Effective algorithms have been devised, to make the device suitable for low-power hardware implementation. After initial prototyping phases (based on microcontrollers and FPGA) VLSI synthesis has been carried out, to estimate actual silicon area and power consumption. Then, extensions of the approach have been foreseen, accounting for multiple sensor management. An example of a low cost embedded heartbeat monitor is discussed.