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Active functional rehabilitation procedures, exoskeleton application and prosthesis employment are essential tools for the society life reinstatement of subjects affected by neuromuscular disorders or victims of serious physical accident. In this paper we propose a closed-loop system for the control of the Functional Electrical Stimulation (FES) based on the Average Threshold Crossing (ATC) event-driven processing technique applied to the sEMG signal. It allows an energy saving, low complex, and real-time muscle activity monitoring suitable for bio-feedback lines applications. Two functional modalities are implemented: self-administered stimulation, in which a person is able to self-stimulate herself/himself, and two-subjects stimulation, that covers the standard rehabilitation flow in which both the therapist and the patient are involved. The system has been tested on 19 healthy subjects to evaluate its performance in term of reproducibility between the voluntary movement and the stimulated one, obtaining a median value of the correlation coefficient, used as similarity measurement, above 0.9 across a wide range of benchmark movements. The promising results confirm the feasibility of controlling myoelectric exoskeleton and prosthesis with the ATC processing approach.

This article presents an assessment of water exchange between sea and the Orbetello Lagoon, Italy. The work methodology is based both on field measurements and on numerical simulations. The assessment is related to tidal, wind and atmospheric pressure for the whole 2016, taken as exemplary year. Field measurements have been conducted by means of level gauges, installed at four internal lagoon points, and one anemometer installed at a central internal point. Moreover, two tidal gages, one located at Civitavecchia harbor and the other at Marina di Campo (Elba Island) have been used to set the offshore boundary conditions for the numerical model. The numerical simulations have been conducted with the HD FM module of the MIKE21 software package, a fully non-linear shallow water solver. The numerical results match closely the field measurements, in terms of total sea-lagoon water exchange, but the former also permits to simulate the whole lagoon hydrodynamics thus allowing much deeper understanding of this physical system. In fact, the numerical simulation has also permitted understanding of the fraction of total water exchange taking place at each of the three lagoon channels connecting the lagoon to the sea.

Thanks to the diffusion of Radio Frequency Identification (RFID) and Quick Response (QR) technologies, distributed and selective Human Machine Interfaces (HMI) are appearing in industrial environments as well as in some museums and other application scenarios. By approaching a smartphone to a suitable tag or symbol, the page of a website is shown and the user can access useful information without any other action. Recently, some indoor localization tools have been proposed, based on Wi-Fi or Bluetooth (BT), locating the smartphone of a user. Even if the uncertainty is quite large, knowing the user's position could allow an automatic viewing of useful information without the need of any action by the user. This paper investigates self-localizing tools with automatic displaying of application-related information about the application, with a special attention to industrial applications, energy, and smart living scenarios. Particularly, this paper investigates the use of BT and sensorized beacons to tag environments and tools. A real case of a cognitive building is presented and discussed.

One of Smart City's goals is to reduce healthcare spending, also by prolonging the active and independent lives of the elderly. Often the elderly live alone; their lifestyle, including proper adherence to therapies, is often controlled by relatives or friends or caregivers by daily or weekly telephone calls. In addition to investigating energy and environment infrastructure and services, the project Brescia Smart Living wants to experience new remote monitoring services. The elderly is constantly monitored by a system of wearable and home sensors; a smartphone, acting as a home gateway, stores and elaborates data and reports events to one or more people chosen by the elderly person. The main result of the project is a very versatile and scalable sensory system, thanks to a framework that simplifies the management of several external devices in Android applications. An ambient assisted living application is presented, showing how the life style of an elderly person can be monitored in a non-invasive way.

Nowadays smartphones and tablets are pervasive in our lives, and are used to fulfil several of our daily tasks. One approach is their usage as sensory systems; by exploiting their embedded sensors, it is possible to get data about users. Typically, such sensors are high end, and it is relatively easy to use them by means of Applications. Data access is handled by Android, which decreases sensor performance, as sampling rate, but greatly eases fruition of such information. On the other hand, many useful sensors are available, today, only externally from the smartphone; typically, they can be connected by Bluetooth Low Energy, a widespread standard for Internet of Things. However, this task is cumbersome for a lot of reasons, e.g. the fact that external sensors can be manufactured by different producers with their own proprietary software approach and solutions. The SAndroidE framework allows an easy connection to external sensors/development kits, managing them and their data as if they were embedded in the smartdevice. This work proposes an experimental characterization of measurements by external sensors, highlighting possibilities and limits of the proposed solution, even in complex scenarios.

Nowadays system architectures based on Internet of Things (IoT) are becoming a pervasive topic in several applications. Smartphones integrate a growing number of high quality sensors and often they are used as gateways for external sensors, wearable sensors or sensors for ambient assisted living. Several recent projects about smart cities and smart living are experiencing sensory data management by means of smartphones, resulting in large data flow and battery power concerns. In these architectures, using sensors embedded within the smartphone is trivial, and several reliable Applications (APPs) are available; on the contrary, APPs managing external sensors have high Source Line Of Code (SLOC), especially if sensors of different vendors are considered and if the APPs need a high level of personalization. In this work, a solution to easily develop Android APPs handling remote sensors, even of different type and manufacturers, is employed in the context of a smart living application. The proposed approach is based on a framework that allows achieving an easy development system architecture, exploiting the Bluetooth 4.0-4.1 technologies. First implementations focused on safety in a Smart City projects are discussed.

The Helping Hand (HH) system is a novel grasp rehabilitation platform aimed at simplifying the clinical usage of wearable electrode arrays for neuromuscular electrical stimulation (NMES). In a randomized dose-matched, clinical study we evaluate usability and effectiveness of the HH treatment, and of other enriched upper limb rehabilitation treatments, and compare the outcomes. This paper shows the preliminary clinical results of the trial on 5 chronic stroke patients throughout a 9 weeks, 3 hours per week, hand preshaping training.

Causal relationship between physical activity and prevention of several diseases has been known for some time. Recently, attempts to quantify dose-response relationship between physical activity and health show that automatic tracking and quantification of the exercise efforts not only help in motivating people but improve health conditions as well. However, no commercial devices are available for weight training and calisthenics. This work tries to overcome this limit, exploiting machine learning technique (particularly Linear Discriminant Analysis, LDA) for analyzing data coming from wearable inertial measurement units, (IMUs) and classifying/counting such exercises. Computational requirements are compatible with embedded implementation and reported results confirm the feasibility of the proposed approach, offering an average accuracy in the detection of exercises on the order of 85%.

Detection of R-peaks in an electrocardiogram (ECG) acquisition is the primary goal of any algorithm for the automatic processing of ECG signals. Several methods have been proposed in the past to accomplish this task, but usually they are designed for the use in clinical situations, where strict real-time requirements are not always needed and availability of devices with high computational resources is not a problem. The recent and broad success of personal and portable devices for health monitoring has opened a new scenario, in which new algorithms need to be developed in order to satisfy constraints due to limited computational resources and the need of having embedded and real-time data processing. This work describes an efficient algorithm for the R-peak detection, designed for low-cost and low-performing portable devices. Performance of the proposed approach, evaluated with a set of well-known ECG waveform, is comparable with traditional methods found in literature. Finally, the successful implementation of the algorithm in a smartphone-based low-cost ECG acquisition system has validated the feasibility of the proposed approach.

Traditional healthcare solutions oblige patients to stay in hospital; bio-signal monitoring and treatment dosing require the presence of specialized personnel. In turn, this scenario implies high-cost, especially for chronic diseases or fragile people surveillance. Wearable sensor-based healthcare solutions may be an effective approach for lowering costs, since they offer good performance and can be used directly at home, e.g., for permanent self-monitoring. In addition, their communication capabilities make easier to exchange information with remotely located professionals. Wearables are smart objects, consisting of sensors, processing units and communication modules, which rely on a smartphone for data collection, display and remote communication. In this paper, authors modify this paradigm moving the computational capability out of the wearable into the smartphone; in this way, raw vital signal stream can be obtained, overcoming possible limitations of the wearable itself. The aim of the work is to verify multichannel capability of the proposed approach, which paves the way to multi-parametric bio-signal analysis. An experimental setup has been arranged using a real-world smartphone and tests have been carried out to evaluate the performance. In particular, it has been verified that three ECG-like signals can be simultaneously collected leveraging on simple multiplexing in the frequency domain.

UAVs platforms are promising for agricultural monitoring since they offer operating flexibility, very high spatial resolution and acquisition costs suitable for frequent on demand monitoring of crop field. In this work we carried out an experimental flight over a rice field in Lombardy region, northern Italy, to test the correlation between reflectance in the spectral channels and vegetation indices derived from imagery acquired with a multi-spectral sensor on board an UAV. Results show that UAV images can be used to map the within-field spatial variability and crop yield (R2~0.42-0.54 between NIR reflectance and/or spectral VIs and rice grain yield) and can successfully complement more traditional technologies for precision farming applications.

Nowadays Spatial Data Infrastructures are the best practice to publish huge amount of spatial data on the Web in an interoperable and distributed way. Nevertheless, this operation requires a significant effort, expertise and motivation to data providers. In this paper, we propose an original approach to support geo-data providers by automating the workflows for publishing geo-data and relative web services for a given application. The prototypal solution has been tested on a real case study to support the regional or national agricultural sector in Italy.

Many research groups are currently working with robotic devices for hand grasp rehabilitation and restoration. A common problem in this area is the fact that existing and commercially available robotic exoskeletons are able to provide gravity compensation of the shoulder and elbow but do not provide any support for the grasping and releasing movements of the hand. The lack of a flexible support technology for the hand reduces the possible ways in which clinicians can deal with the issue of a personalized, effective rehabilitation. This paper presents new software that allows FES assisted grasping to integrate with the ArmeoSpring (Hocoma AG). The system uses a Man-In-The-Loop control approach, whereby surface EMG signals from proximal muscles are used to trigger and modulate multichannel FES applied to distal muscles, thus allowing patient induced and strength adapted movement control of the hand. Combining volitionally controlled FES with arm-weight-compensation allows early adoption of FES assisted therapy for patients, augmenting their functionalities and extending training capabilities with the ArmeoSpring.

The wider and wider availability of powerful, low-cost mobile devices (e.g., smartphones or tablets) is deeply changing healthcare, so that the mHealth term has been coined. The announcement of healthcare projects by market big players as Apple and Samsung confirms this trend. In particular, the opportunity to collect reliable patient data automatically allows to enhance patient/user self-management and helps in better delivering therapies. In this paper, authors propose an innovative architecture for a smart pill-dispenser enhanced by a smartdevice that furnishes the capability of automatically identifying the user, other than logging medicine in-take activities. A real-world prototype, based on an emulated pill-dispenser connected via an NFC link to different smartdevices, has been purposely realized. Experimental tests confirm the architecture feasibility. Low-cost requirements are satisfied and a user-friendly interface has been implemented.

This paper describes a mapping project carried out using both optical and SAR data on an agricultural area in northern Italy where the main crops are corn, rice and wheat. Temporal trends of backscatter and reflectance, given by the variations in vegetation growth, soil conditions and agricultural practices were analyzed and interpreted thanks to the ground-measured data. Information extracted from both optical and SAR data (vegetation indices, backscatter and texture features) were used to create training sets for implementing three different classification approaches. The work aimed at comparing early crop maps with maps derived at the end of the season. Results show that the classification accuracy obtained using only multispectral optical data is higher than the one reached using only SAR as input. Integrating both optical and SAR multitemporal features provides some advantages in terms of a more reliable crop map, especially during an early temporal stage scenario. Among the supervised algorithms tested, Maximum Likelihood shows the best overall accuracy performances at each thematic level, time step and using both optical and SAR input data.

Muscle synergies have been proposed as building blocks that simplify the construction of movements, and as a method to study motor behavior. However, the pre-processing of the EMG signals and the factorization algorithm may impact on their extraction and meaning. This preliminary work aimed at investigating the influence of the selection of the muscles on muscle synergies analysis. In particular, muscle synergies have been extracted from datasets including different upper limb muscles, recorded during the execution of planar reaching and grasping movements. Results suggested that the amount of degrees of freedom activated by the muscles considered in the analysis determines the number of muscle synergies, while muscle redundancy does not affect muscle synergies extraction. In conclusion, the number of muscle synergies is influenced not only by the task constraints and the performance of the movement, but also by the choice of the muscles. Therefore, both aspects have to be carefully considered to design muscle synergies analysis and to compare results from different studies.

The dynamic time warping approach is utilised to obtain a template from the left ventricular volume signal. The technique, by performing shrinking and enlarging of the temporal axes of two waveforms, indicates the best non-linear alignment between them. In this application this technique is used to track on a cycle-by-cycle basis the diastasis onset and offset thus allowing one to reliably measure temporal sub-intervals of the filling period.

The purpose of this study was to identify the main causes for whisker growth and to establish its mechanism, while at the same time identifying test conditions that can be used for an industry wide acceptance test for whisker formation, since this test has never been established. In order to understand the influence of humidity and corrosion on whisker growth the E4 (Freescale Semiconductor, Infineon, STMicroelectronics and Philips) started an extensive design of experiments (DoE). Parameters in this DoE include: plating, solderpaste, flux, substrate material, preconditioning and condensation. This paper presents the outcome of this DoE and furthermore give an update on the previously reported and ongoing experiments to discover the main factors influencing whisker growth.

Whisker growth on electroplated tin finishes may cause failures in electronic equipment. Although the mechanism for whisker growth is not understood in all details, different environmental conditions promote whisker growth in tin plated on Cu-based substrates and tin plated on FeNi42. In FeNi42 leadframes the thermal mismatch of tin and FeNi42 most likely promotes whisker formation and growth. This is the first systematic investigation of various temperature cycling conditions on whisker growth rates in Sn on FeNi42. The results indicate a decay of the growth rate with the number of cycles and/or whisker length and a linear relationship between whisker length and /spl Delta/T. In addition, second level assembly appears to reduce the maximum whisker length for a given environmental condition.

The authors deal with the design of V-band up/down converters for spaceborne communication systems. The design of a 60/12 GHz downconverter and the design of the corresponding 12/64 parametric upconverter intended to be used in these communication systems are described. The upconverter test results and the main performance data of the upconverter are summarized. The down converter conversion loss and noise figures are presented. >

A procedure to automatically extract the beat-to-beat series of well-known clinical parameters from the left ventricular volume signal obtained by the acoustic quantification technique is validated in normal subjects and chronic heart failure patients (NYHA class II-III). The end systolic and diastolic volume, the contribution of the atrial contraction to the ventricular filling and the ejection fraction provide a vivid distinction between normal and pathological subjects. However a large amount of variability at the respiratory frequency is observed. The respiratory-related variability of the left ventricular volume parameters could be related to the capability of the heart to adapt to changes of the venous return and, therefore, could be used as an index of the cardiac mechanical efficiency.