Showing papers by "Gian Marco Revel published in 2016"

An IoT-inspired cloud-based web service architecture for e-Health applications

Abstract: E-Health services can take advantage of the technological achievements in the area of the Internet of Things (IoT), and of the cost reduction and increasing user-friendliness of health monitoring devices. Homes equipped with environmental sensors, physiological parameters monitoring devices, and home automation devices, could become the “hardware” of an “operating system” for application developers and service providers. The system would expose web services through a unique cloud infrastructure for users' data collection and storage, administration and billing, and healthcare service provisioning applications by possibly multiple third parties. We present an instance of a cloud-based web server which relies on a “home system” for the collection of information from an heterogeneous set of devices, providing a high level description of the proposed overall architectural model, of the induced opportunities from the market perspective, and of how it could be used by healthcare applications developers and service providers, including details on how the web server Application Programming Interfaces (API) is implemented in our instance.

Smart Monitoring of Userźs Health at Home: Performance Evaluation and Signal Processing of a Wearable Sensor for the Measurement of Heart Rate and Breathing Rate

Abstract: Nowadays, the monitoring of users’ health status is possible by means of smart sensing devices at low-cost and with high measuring capabilities. Wearable devices are able to acquire multiple physiological and physical waveforms and are equipped with on-board algorithms to process these signals and extract the required quantities. However, the performance of such processing techniques should be evaluated and compared to different approaches, e.g. processing of the raw waveforms acquired. In this paper, the authors have performed a metrological characterization of a commercial wearable monitoring device for the continuous acquisition of physiological quantities (e.g. Heart Rate - HR and Breathing Rate - BR) and raw waveforms (e.g. Electrocardiogram - ECG). The aim of this work is to compare the performance of the on-board processing algorithms for the calculation of HR and BR with a novel approach applied to the raw signals. Results show that the HR values provided by the device are accurate enough (±2.1 and ±2.8 bpm in static and dynamic tests), without the need of additional processing. On the contrary, the implementation of the dedicated processing technique for breathing waveform allows to compute accurate BR values (±2.1 bpm with respect to standard equipment).

Implementation of an “at-home” e-Health system using heterogeneous devices

Abstract: Systems enabling long-term monitoring of physiological data and everyday activities has been the subject of considerable research efforts in the last years, in order to improve the quality of life of patients, elderly people and common citizens at home, out of the hospitalization. With the availability of inexpensive, low power, wireless and integrated devices, current smart homes are typically equipped with a large amount of sensors, which collaboratively process and make deductions from the acquired data on the state of the home, as well as the activities and behaviors of its residents. According to the field of application and the end-users involved (healthy people, elderly, people with disabilities), the definition of the parameters (e.g. heart rate, blood pressure, activity, body mass, etc.) and the appropriate sensors (electrocardiogram, sphygmomanometer, glucometer, etc.) for their acquisition assume a fundamental role. One of the goals of the Italian project Health@Home is to create a network of health sensors and home automation devices to monitor the user's status within the home environment. We present a candidate implementation of such a system, describing the software architecture and the selected components, and a testbed of the architecture, realized in a lab room and used for a preliminary experimental study involving seven users.

Methodologies for continuous activity classification of user through wearable devices: Feasibility and preliminary investigation

Abstract: Nowadays, the possibility to use wearable devices at low-cost and with high measuring capabilities is widely spread. They are able to collect multiple physiological and physical quantities, but the information provided is generally of "low level" (series of raw data) for the end-user, caregiver or the medical staff. A smart processing and an intelligent combination of these data may lead to the identification of more refined indicators (e.g. the kind of activity performed by the user) and so easier to understand information. In this work, the authors have performed a preliminary investigation on how to discriminate different levels of physical activity (resting, walking, running) conducted by a user. The analysis has been carried out with different trials and the use of a commercial wearable sensor (BioHarness 3.0), which measures five physiological and physical quantities. Different approaches, based on classification and clustering techniques, have been tested to prove their ability in discriminating the tasks performed. Results show that a threshold-based approach, applied to the physical quantities measured, is able to identify the activities conducted by the user, with an average accuracy of 98%. Moreover, a novel indicator based on the combination of both physiological and physical quantities is proposed and discussed in the paper. The integration of physiological data (e.g. Heart Rate) could lead to more "high level" information, which should be better investigated in future works.

Towards a Comprehensive Asset Integrity Management (AIM) Approach for European Infrastructures

Abstract: Transport infrastructure is the backbone of national economies, providing connections for people and goods, access to jobs and services, and enabling trade and economic growth. It is of paramount importance to preserve, maintain and upgrade the infrastructure network so that to sustain the economic growth and an intelligent mobility. Asset Integrity Management (AIM) approaches will therefore represent key tools for facing the infrastructure maintenance issue and for tackling the ageing that characterize already existing assets. This paper, starting from analyzing the current state of the art solutions in assets management (Enevoldsen, I., 2008), proposes a comprehensive AIM approach that aims at replacing current time-based approaches with a performance-based approach that can systematically take into account the dynamic nature of the transport network. This means moving from a deterministic to a probabilistic approach in design, rehabilitation and retrofitting of infrastructures for increasing life-time and reducing maintenance costs. Such approach therefore laid the basis of secure sustainable impact since by improving awareness and reducing uncertainties, it might allow achieving an optimal balance among available resources and planning of investments.

Laser ultrasonics for bulk-density distribution measurement on green ceramic tiles

Abstract: In this paper a Laser Ultrasonics (LUT) system is developed and applied to measure bulk density distribution of green ceramic tiles, which are porous materials with low heat conductivity. Bulk density of green ceramic bodies is a fundamental parameter to be kept under control in the industrial production of ceramic tiles. The LUT system proposed is based on a Nd:YAG pulsed laser for excitation and an air-coupled electro-capacitive transducer for detection. The paper reports experimental apparent bulk-density measurements on white ceramic bodies after a calibration procedures. The performances observed are better than those previously achieved by authors using air-coupled ultrasonic probes for both emission and detection, allowing to reduce average uncertainty down to about ±6 kg/m3 (±0.3%), thanks to the increase in excitation efficiency and lateral resolution, while maintaining potential flexibility for on-line application. The laser ultrasonic procedure proposed is available for both on-line and off-line application. In this last case it is possible to obtain bulk density maps with high spatial resolution by a 2D scan without interrupting the production process.

Stationary Wavelet Transform for denoising Pulsed Thermography data: optimization of wavelet parameters for enhancing defects detection

Abstract: Innovative denoising techniques based on Stationary Wavelet Transform (SWT) have started being applied to Pulsed Thermography (PT) sequences, showing marked potentialities in improving defect detection. In this contribution, a SWT-based denoising procedure is performed on high and low resolution PT sequences. Samples under test are two composite panels with known defects. The denoising procedure undergoes an optimization step. An innovative criterion for selecting the optimal decomposition level in multi-scale SWT-based denoising is proposed. The approach is based on a comparison, in the wavelet domain, of the information content in the thermal image with noise propagated. The optimal wavelet basis is selected according to two performance indexes, respectively based on the probability distribution of the information content of the denoised frame, and on the Energy-to-Shannon Entropy ratio. After the optimization step, denoising is applied on the whole thermal sequence. The approximation coefficients at the optimal level are moved to the frequency domain, then low-pass filtered. Linear Minimum Mean Square Error (LMMSE) is applied to detail coefficients at the optimal level. Finally, Pulsed Phase Thermography (PPT) is performed. The performance of the optimized denoising method in improving the defect detection capability respect to the non-denoised case is quantified using the Contrast Noise Ratio (CNR) criterion.

Continuous scanning laser Doppler vibrometry and wavelet processing for diagnostics: A time domain approach

Abstract: Continuous Scanning Laser Doppler Vibrometry (CSLDV) is a well-known technique within the structural dynamic community. However, the whole potentials of CSLDV for diagnostic purposes have not been fully exploited yet. This paper presents a time domain approach for identifying damages in structures. The method, which is based on a wavelet processing of vibration data collected by CSLDV, does not need any a-priori knowledge of the vibration behavior of the undamaged sample. Applications on real test cases are presented and discussed in the paper, demonstrating the promising performance of the approach as a non-destructive testing technique.

XDEM for Tuning Lumped Models of Thermochemical Processes Involving Materials in the Powder State

Abstract: Processes involving materials in gaseous and powder states cannot be modelled without coupling interactions between the two states. XDEM (Extended Discrete Element Method) is a valid tool for tackling this issue, since it allows a coupled CFD-DEM simulation to be run. Such strength, however, mainly finds in long computational times its main drawback. This aspect is indeed critical in several applications, since a long computational time is in contrast with the increasing demand for predictive tools that can provide fast and accurate results in order to be used in new monitoring and control strategies. This paper focuses on the use of the XDEM framework as a tool for fine tuning a lumped representation of the non-isothermal decarbonation of a CaCO3 sample in powder state. The tuning of the lumped model is performed exploiting the multi-objective optimization capability of genetic algorithms. Results demonstrate that such approach makes it possible to estimate fast and accurate models to be used, for instance, in the fields of virtual sensing and predictive control.

Stationary Wavelet Transform denoising in Pulsed Thermography: influence of camera resolution on defect detection

Abstract: Denoising filters are widely used in image enhancement. However, they might induce severe blurring effects the lower is the resolution of the original image. When applied to a thermal image in Non-Destructive Testing (NDT), blurring could entail wrong estimation of defect boundaries and an overall reduction in defect detection performances. This contribution discusses the application of a wavelet-based denoising technique to a thermographic sequence obtained from a Pulsed Thermography testing, when using a highresolution 1024x768 FPA infrared camera. Influence of denoising approach on data postprocessed by Principal Component Analysis is discussed. Results indicate marked enhancement in defect detection, especially when compared to those obtained with a standard-resolution 320x240 FPA infrared camera.