Eurohaptics 2018a major international conference on haptics and touch enabled computer applications and the primary European meeting for researchers in this field - Pisa, Italy, June 13-16, 2018
Head of Industrial Informatics Lab of the Department of Electrical and Information Engineering of Polytechnic University of Bari.
BIO
ABOUT ME
Vitoantonio Bevilacqua is Associate Professor of Electronic and Information Bioengineering at the Department of Electrical and Information Engineering of Polytechnic University of Bari where obtained the Laurea Degree in Electronic Engineering, Ph.D. in Electrical Engineering and Post-Doc in Industrial Informatics, teaches Human Machine Interaction and Bioinformatics and Big Data Analytics and is the Head of Industrial Informatics Lab. Since 1996 he has been working and investigating in the field of computer vision and image processing, bioengineering, human-machine interaction based on machine learning and soft computing techniques (neural networks, evolutionary algorithms, hybrid expert systems, deep learning). The main applications of his research are in medicine, in biometry, in bioinformatics in ambient assisted living and industry. In 2000 he was involved as Visiting Researcher in an EC funded TMR (Trans-Mobility of Researchers) network (ERB FMRX-CT97-0127) called CAMERA (CAd Modeling Environment from Range Images) and worked in Manchester (UK) at UK Robotics Ltd, in the field of geometric feature extraction and 3D objects reconstruction. He has published more than 150 papers in refereed journals, books, international conferences proceedings and chaired several sessions such as Speech Recognition, Biomedical Informatics, Intelligent Image Processing and Bioinformatics in international conferences. On July 2011, he was invited as lecturer at International School on Medical Imaging using Bioinspired and Soft Computing-Miere (Spain) MIBISOC FP7-PEOPLE-ITN-2008. GA N. 238819— where presented his research on Intelligent Tumors Computer Aided Early Diagnosis and Therapy: Neural Network and Genetic Algorithms frameworks.
RESEARCH
In 2000 he was involved as Visiting Researcher in an EC funded TMR (Trans-Mobility of Researchers) network (ERB FMRX-CT97-0127) called CAMERA (CAd Modeling Environment from Range Images) and worked in Manchester (UK) at UK Robotics Ltd, in the field of geometric feature extraction and 3D objects reconstruction. He has published more than 130 papers in refereed journals, books, international conferences proceedings and chaired several sessions such as Speech Recognition, Biomedical Informatics, Intelligent Image Processing and Bioinformatics in international conferences. On July 2011, he was invited as lecturer at International School on Medical Imaging using Bioinspired and Soft Computing-Miere (Spain) MIBISOC FP7-PEOPLE-ITN-2008. GA N. 238819— where presented his research on Intelligent Tumors Computer Aided Early Diagnosis and Therapy: Neural Network and Genetic Algorithms frameworks.
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RESUME
EDUCATION
19911996
Bari
Laurea in Ingegneria Elettronica (Indirizzo AUTOMATICA)
Politecnico di Bari
19962000
Bari
Dottore di Ricerca (PhD) in Ingegneria Elettrotecnica (Curriculum: Automazione Industriale)
Politecnico di Bari
20002000
Bari
Scuola Interateneo di Elaborazione del Segnale
Politecnico di Bari/Università degli Studi di Bari
20002000
Manchester (UK).
Visiting Researcher progetto - Progetto CAMERA
UK Robotics Ltd
ACADEMIC AND PROFESSIONAL POSITIONS
20092010
SAN FRANSICO
GRADUATE STUDENT RESEARCHER
OXFORD UNIVERSITY COMPUTING LABORATORY
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20092010
SAN FRANSICO
LAB ASSISTANT
OXFORD UNIVERSITY COMPUTING LABORATORY
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20082009
SAN FRANSICO
RESEARCH ASSISTANT
UNIVERSITY OF NANTES
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HONORS AND AWARDS
2004
Malaysia
Speech Recognition Session Chair
- International Conference on Artificial Intelligence in Engineering and Technology (ICAIET 2004).
2004
USA
Biomedical Informatics Session Chair
International Conference on Advances in Computer Science and Technology (ACST USA)
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2006
CINA
Program Committee Member
ICIC
2007
CINA
Publicity Co-Chair
ICIC
2008
CINA
Program Committee Member
ICIC
2008
CINA
Publication Chair
ICIC
2011
CINA
Tutorial Chair
ICIC
2012
CINA
Publication Chair
ICIC
20132014
CINA
Special Sessions/Workshop Chair
ICIC
2015
CINA
Special Issue Co-Chair
ICIC
2016
CINA
Program Committee Co-Chair
ICIC
2017
CINA
Award Committee Co-Chair
ICIC
2007
Program Committee Member
International Conference on Life System Modeling and Simulation
2009
Korea
Program Co-Chair
International Conference on Intelligent Computer
2011
ITALY
Program Committee Co-Chair
IEEE MeMEA 2011
2015
ITALY
General Chair
Human Machine Interaction Summer School
2015
Ireland
Program Committee Member
IEEE International Joint Conference on Neural Networks (IJCNN 2015),
2015
ITALY
Program Committee Member
WIVACE 2015
2016
CINA
Program Co-Chair
International Conference on Intelligent Computer - ICIC
2016
ITALY
Program Co-Chair
2016 IEEE Italy Section Medical Informatics Summer School
2016
ITALY
Program Committee Member
WIVACE 2016
2016
INDIA
Program Chair.
International Conference on Recent Trends in Image Processing & Pattern Recognition (RTIP2R)
2016
USA
Program Committee Member
IEEE International Joint Conference on Neural Networks (IJCNN 2017)
First Progresses in Evaluation of Resonance in Staff Selection through Speech Emotion Recognition
ICIC 2013
Speech Emotion Recognition (SER) is a hot research topic in the field of Human Computer Interaction. In this paper a SER system is developed with the aim of providing a classification of the “state of interest” of a human subject involved in a job interview. Classification of emotions is performed by analyzing the speech produced during the interview. The presented methods and results show just preliminary conclusions, as the work is part of a larger project including also analysis, investigation and classification of facial expressions and body gestures during human interaction. At the current state of the work, investigation is carried out by using software tools already available for free on the web; furthermore, the features extracted from the audio tracks are analyzed by studying their sensitivity to an audio compression stage. The Berlin Database of Emotional Speech (EmoDB) is exploited to provide the preliminary results.
Artificial IntelligenceComputer VisionVitoantonio Bevilacqua, Pietro Guccione, Luigi Mascolo, Pasquale Pio Pazienza, Angelo Antonio Salatino, Michele Pantaleo
First Progresses in Evaluation of Resonance in Staff Selection through Speech Emotion Recognition
Vitoantonio Bevilacqua, Pietro Guccione, Luigi Mascolo, Pasquale Pio Pazienza, Angelo Antonio Salatino, Michele Pantaleo
Artificial IntelligenceComputer Vision
05Aug2013
An Evolutionary Optimization Method for Parameter Search in 3D Points Cloud Reconstruction
ICIC 2013
Reconstruction of 3D laser scanned point clouds may generate a mesh characterized by a high number of triangles. Unfortunately, in Computer Aided Design environments neither a simple triangle reduction, nor decimation filters are feasible for mesh optimization, because of their intrinsic errors.
In this paper we show how Genocop III can be effectively used to reconstruct a point cloud bounding the error under a certain threshold. Moreover, we define an optimized algorithm for evaluating the reconstruction error, that exploits AABB-trees and pre-computation and provides a useful metric to the genetic algorithm.
Clustering and Assembling Large Transcriptome Datasets by EasyCluster2
ICIC 2013
EasyCluster is a well-established python software appropriately developed to produce reliable clusters by expressed sequence tags (EST) in order to infer and improve gene structures as well as discover potential alternative splicing events. In the present work we present EasyCluster2, a reimplementation of EasyCluster in Java programming language, able to manage genome scale transcriptome data produced by Roche 454 sequencers. EasyCluster2 has been developed to speed up the creation of gene-oriented clusters and facilitate downstream analyses as the assembly of full-length transcripts. In addition, EasyCluster2 can employ known annotations to refine the overall clustering procedure, embeds the AStalavista software to predict the impact of alternative splicing per cluster and provides output files in specific formats to be uploaded in the UCSC genome browser for an easy browsing of results. Thanks to the user-friendly interface, EasyCluster2 simplifies the interpretation of findings to researchers with no specific skills in bioinformatics. Easycluster2 executable is freely available at https://code.google.com/p/easycluster2/.
Attention Control during Distance Learning Sessions
ICIAP Workshops 2013
The distance learning (DL) is a teaching system that extends the education beyond the physical barriers, providing access to remote places and disabilities. The increasing need of procedures for DL certification is now involving biometric approach. An analysis of biometric techniques is shown in order to ensure the users authentication, to verify the individual’s attention level and then to certificate the learning outcomes. That is necessary to implement a system to identify uniquely the users and to track both path’s carried (visited pages) and use’s time, to have a secure users identification and also validation of the environments conditions in which they take place during possible tests of certification. The appropriate biometric technique is appeared the Face Recognition because it allows a real-time verification of the real presence, low implementation costs by use of webcam and reasonable degree of reliability. To avoid the influence related to environmental conditions, it has been realized a modular system that implements Detection and Recognition operations. The implemented system is able to verify the presence of learners beyond the screen during lessons or learning tests, to allow authentication and to verify the simultaneous presence of other individuals in order to start an alarm if unregistered peoples are present during learning or testing sessions. This system is also capable to recognize the attention level of users through Request Random Windows (RRW). The application opens casually a RRW in different screen position during the DL and asks learner to click upon to close it within a few seconds. When this window is closed, a new step of Face Recognitions is performed again to validate the presence of the same user. Interesting results are obtained in experimental cases employing these techniques on a individuals samples set.
Three-dimensional virtual colonoscopy for automatic polyps detection by artificial neural network approach: New tests on an enlarged cohort of polyps
Neurocomputing
In computer aided diagnosis (CAD) tools searching for colonrectal polyps and based on three dimensions virtual colonoscopy (3DVC) using computed tomography (CT) images, the reduction of the occurrence of false-positives (FPs) still represents a challenge because they are source of unreliability. Following an encouraging previous supervised approach Bevilacqua et al., Three-dimensional Virtual Colonoscopy for Polyps Detection by Supervised Artificial Neural Networks D.-S. Huang et al. (Eds.): ICIC, LNBI 6840, Springer-Verlag, Berlin Heidelberg, (2011), pp. 596–603, the aim of this work is to discuss, in details, how the adopted strategies, designed and tested on an initial reduced data set, reveals good performance and robustness in terms of FPs reduction on an enlarged cohort of new cases.
Three-dimensional virtual colonoscopy for automatic polyps detection by artificial neural network approach: New tests on an enlarged cohort of polyps
Vitoantonio Bevilacqua
Artificial IntelligenceComputer VisionMedical CAD
05May2013
Asymmetry measurement for vibroactive correction in lower limbs mobility.
Comput. Sci. Inf. Syst.
We developed a wearable device to improve lower limb sport training. The presented system consists of a pair of spandex shorts which embed a processor unit, 2 accelerometers and 2 vibro motors. The accelerometers are located in proximity to the knees and measure tri-axial accelerations. We present a novel method to compute and correct asymmetry of lower limbs during training. The user performs an initial calibration phase which sets the accelerometer reference frames. While running, the system continuously refines the calibration using principal component analysis to take in account occasional shorts assessments. The system activates a corrective vibro feedback on the specific leg according to an asymmetry metric based on: (i) foot ground impact, (ii) phase error between legs, (iii) transversal knee movements. User tests demonstrated that the device is ergonomic to wear, easy to use and the corrective vibro feedback is appreciated during the training.
RehabilitationMichele Fiorentino, Antonio E. Uva, Mario Massimo Foglia, Vitoantonio Bevilacqua
Asymmetry measurement for vibroactive correction in lower limbs mobility.
Michele Fiorentino, Antonio E. Uva, Mario Massimo Foglia, Vitoantonio Bevilacqua
Rehabilitation
05May2013
Scalable high-throughput identification of genetic targets by network filtering
BMC Bioinformatics
Discovering the molecular targets of compounds or the cause of physiological conditions, among the multitude of known genes, is one of the major challenges of bioinformatics. One of the most common approaches to this problem is finding sets of differentially expressed, and more recently differentially co-expressed, genes. Other approaches require libraries of genetic mutants or require to perform a large number of assays. Another elegant approach is the filtering of mRNA expression profiles using reverse-engineered gene network models of the target cell. This approach has the advantage of not needing control samples, libraries or numerous assays. Nevertheless, the impementations of this strategy proposed so far are computationally demanding. Moreover the user has to arbitrarily choose a threshold on the number of potentially relevant genes from the algorithm output.
Data MiningVitoantonio Bevilacqua, Paolo Pannarale
Scalable high-throughput identification of genetic targets by network filtering
Vitoantonio Bevilacqua, Paolo Pannarale
Data Mining
25Jun2014
Fall detection in indoor environment with kinect sensor
INISTA 2014
Falls are one of the major risks of injury for elderly living alone at home. Computer vision-based systems offer a new, low-cost and promising solution for fall detection. This paper presents a new fall-detection tool, based on a commercial RGB-D camera. The proposed system is capable of accurately detecting several types of falls, performing a real time algorithm in order to determine whether a fall has occurred. The proposed approach is based on evaluating the contraction and the expansion speed of the width, height and depth of the 3D human bounding box, as well as its position in the space. Our solution requires no pre-knowledge of the scene (i.e. the recognition of the floor in the virtual environment) with the only constraint about the knowledge of the RGB-D camera position in the room. Moreover, the proposed approach is able to avoid false positive as: sitting, lying down, retrieve something from the floor. Experimental results qualitatively and quantitatively show the quality of the proposed approach in terms of both robustness and background and speed independence.
A new tool for gestural action recognition to support decisions in emotional framework.
INISTA 2014
Introduction and objective: the purpose of this work is to design and implement an innovative tool to recognize 16 different human gestural actions and use them to predict 7 different emotional states. The solution proposed in this paper is based on RGB and depth information of 2D/3D images acquired from a commercial RGB-D sensor called Kinect. Materials: the dataset is a collection of several human actions made by different actors. Each action is performed by each actor for three times in each video. 20 actors perform 16 different actions, both seated and upright, totalling 40 videos per actor. Methods: human gestural actions are recognized by means feature extractions as angles and distances related to joints of human skeleton from RGB and depth images. Emotions are selected according to the state-of-the-art. Experimental results: despite truly similar actions, the overall-accuracy reached is approximately 80%. Conclusions and future works: the proposed work seems to be back-ground- and speed-independent, and it will be used in the future as part of a multimodal emotion recognition software based on facial expressions and speech analysis as well.
Artificial IntelligenceComputer VisionVitoantonio Bevilacqua, Donato Barone, Francesco Cipriani, Gaetano D'Onghia, Giuseppe Mastrandrea, Giuseppe Mastronardi, Marco Suma, Dario D'Ambruoso
A new tool for gestural action recognition to support decisions in emotional framework.
Vitoantonio Bevilacqua, Donato Barone, Francesco Cipriani, Gaetano D'Onghia, Giuseppe Mastrandrea, Giuseppe Mastronardi, Marco Suma, Dario D'Ambruoso
Artificial IntelligenceComputer Vision
11Jul2014
A novel BCI-SSVEP based approach for control of walking in Virtual Environment using a Convolutional Neural Network
IJCNN 2014
A non-invasive Brain Computer Interface (BCI) based on a Convolutional Neural Network (CNN) is presented as a novel approach for navigation in Virtual Environment (VE). The developed navigation control interface relies on Steady State Visually Evoked Potentials (SSVEP), whose features are discriminated in real time in the electroencephalographic (EEG) data by means of the CNN. The proposed approach has been evaluated through navigation by walking in an immersive and plausible virtual environment (VE), thus enhancing the involvement of the participant and his perception of the VE. Results show that the BCI based on a CNN can be profitably applied for decoding SSVEP features in navigation scenarios, where a reduced number of commands needs to be reliably and rapidly selected. The participant was able to accomplish a waypoint walking task within the VE, by controlling navigation through of the only brain activity.
Artificial IntelligenceVitoantonio Bevilacqua, Giacomo Tattoli, Domenico Buongiorno, Claudio Loconsole, Daniele De Leonardis, Michele Barsotti, Antonio Frisoli, Massimo Bergamasco
A novel BCI-SSVEP based approach for control of walking in Virtual Environment using a Convolutional Neural Network
Vitoantonio Bevilacqua, Giacomo Tattoli, Domenico Buongiorno, Claudio Loconsole, Daniele De Leonardis, Michele Barsotti, Antonio Frisoli, Massimo Bergamasco
Artificial Intelligence
15Jul2014
A Multimodal Fingers Classification for General Interactive Surfaces
ICIC 2014
In this paper a multimodal fingers classification to detect touch points over general interactive surfaces is presented. Three different classifiers have been used: artificial neural networks, decision trees and rules learner. The data set has been created extracting statistical parameters from finger ROIs on about 40000 video samples. The accuracy obtained for the three classifiers on the test set is respectively 96,68%, 96,58% and 97,41%. The model classifiers generated work very well in real-time applications, so an innovative software called TouchPAD has been designed and implemented.
Artificial IntelligenceComputer VisionVitoantonio Bevilacqua, Donato Barone, Marco Suma
A Multimodal Fingers Classification for General Interactive Surfaces
Vitoantonio Bevilacqua, Donato Barone, Marco Suma
Artificial IntelligenceComputer Vision
15Jul2014
Real-Time Emotion Recognition: An Improved Hybrid Approach for Classification Performance.
ICIC 2014
Emotions, and more in detail facial emotions, play a crucial role in human communication. While for humans the recognition of facial states and their changes is automatic and performed in real-time, for machines the modeling and the emulation of this natural process through computer vision-based approaches are still a challenge, since real-time and automation system requirements negatively affect the accuracy in emotion detection processes.
In this work, we propose an approach which improves the classification performance of our previous computer vision-based algorithm for facial feature extraction and automatic emotion recognition. The proposed approach integrates the previous one adding six geometrical and two appearance-based features, still meeting the real-time requirement. As result, we obtain an improved processing pipeline classifier (classification accuracy incremented up to 6-7%) which allows the recognition of eight facial emotions (six basic Ekman’s emotions plus Contemptuous and Neutral).
Artificial IntelligenceComputer VisionClaudio Loconsole, Domenico Chiaradia, Vitoantonio Bevilacqua, Antonio Frisoli
Real-Time Emotion Recognition: An Improved Hybrid Approach for Classification Performance.
Claudio Loconsole, Domenico Chiaradia, Vitoantonio Bevilacqua, Antonio Frisoli
Artificial IntelligenceComputer Vision
15Jul2014
Evaluation of Resonance in Staff Selection through Multimedia Contents
ICIC 2014
In this paper we present the results of an experimental Italian research project finalized to support the classification process of the two behavioural status (resonance and dissonance) of a candidate applying for a job position. The proposed framework is based on an innovative system designed and implemented to extract and process the non-verbal expressions like facial, gestural and prosodic of the subject, acquired during the whole job interview session. In principle, we created our own database, containing multimedia data extracted, by different software modules, from video, audio and 3D sensor streams and then used SVM classifiers that perform in terms of accuracy 72%, 79% and 63% respectively for facial, vocal and gestural features. ANN classifiers have also been used, obtaining comparable results. Finally, we combined all the three domains and then reported the results of this last classification test proving that the experimental proposed work seems to perform in a very encouraging way.
Artificial IntelligenceComputer VisionVitoantonio Bevilacqua, Angelo Antonio Salatino, Carlo Di Leo, Dario D'Ambruoso, Marco Suma, Donato Barone, Giacomo Tattoli, Domenico Campagna, Fabio Stroppa, Michele Pantaleo
Evaluation of Resonance in Staff Selection through Multimedia Contents
Vitoantonio Bevilacqua, Angelo Antonio Salatino, Carlo Di Leo, Dario D'Ambruoso, Marco Suma, Donato Barone, Giacomo Tattoli, Domenico Campagna, Fabio Stroppa, Michele Pantaleo
Artificial IntelligenceComputer Vision
15Jul2014
Evolutionary Design of Synthetic Gene Networks by Means of a Semantic Expert System
ICIC 2014
In the last decade many researchers proposed tools and methods for the automatic generation of synthetic biological devices with desired functions. However, advances in synthetic biology have been limited by a lack of frameworks meeting the essential requirements of standardization, modularity, complexity and re-use. The present work tries to cope with the standardization issue by the adoption of model exchange standards like CellML, BioBrick standard biological parts and standard signal carriers for modeling purpose. The generated models are made of SVP modular components. Model complexity includes more interaction dynamics than previous works. The inherent software complexity has been handled by a rational use of ontologies and rule engine. The database of parts and interactions is automatically created from publicly available whole system models. Built on this automatic modeling component, a genetic algorithm has been implemented, that searches the space of possible genetic circuits for an optimal circuit meeting user defined input-output dynamics. The system has been successfully tested on two test cases. This work proposes a new approach able of pushing forward the complexity managed by genetic circuits automatic design tools.
Paolo Pannarale, Vitoantonio Bevilacqua
Evolutionary Design of Synthetic Gene Networks by Means of a Semantic Expert System
Paolo Pannarale, Vitoantonio Bevilacqua
10Oct2014
A Robust Real-Time 3D Tracking Approach for Assisted Object Grasping
EuroHaptics 2014
Robotic exoskeletons are being increasingly and successfully used in neuro-rehabilitation therapy scenarios. Indeed, they allow patients to perform movements requiring more complex inter-joint coordination and gravity counterbalancing, including assisted object grasping. We propose a robust RGB-D camera-based approach for automated tracking of both still and moving objects that can be used for assisting the reaching/grasping tasks in the aforementioned scenarios. The proposed approach allows to work with non pre-processed objects, giving the possibility to propose a flexible therapy. Moreover, our system is specialized to estimate the pose of cylinder-like shaped objects to allow cylinder grasps with the help of a robotic hand orthosis.
To validate our method both in terms of tracking and of reaching/grasping performances, we present the results achieved conducting tests both on simulations and on real robotic-assisted tasks performed by a patient.
Computer VisionRoboticsClaudio Loconsole, Fabio Stroppa, Vitoantonio Bevilacqua, Antonio Frisoli
A Robust Real-Time 3D Tracking Approach for Assisted Object Grasping
Claudio Loconsole, Fabio Stroppa, Vitoantonio Bevilacqua, Antonio Frisoli
Computer VisionRobotics
25Feb2014
An interaction torque control improving human force estimation of the rehab-exos exoskeleton
HAPTICS 2014
This paper describes the interaction torque control of the Rehab-Exos, an upper-limb robotic exoskeleton with direct torque joint sensors for interaction in Virtual Environments and rehabilitation. The control architecture consists in a centralized torque control and separated optimal torque observers for each joint of the exoskeleton. The optimal observer is a full-state Kalman filter providing the estimates of both internal and external torques acting on the joints and overcoming most of the issues due to the noise in the torque sensor signals. The centralized torque control is based on a full dynamics model of the exoskeleton, calculates the kinematics and dynamics of the system and estimates the feed-forward contribution for the compensation of dynamic loads measured by joint torque sensors. Experimental tests have been carried out to validate the desired torque tracking in haptic interaction tasks.
A semantic expert system for the evolutionary design of synthetic gene networks
GECCO (Companion) 2014
This work tries to cope with the standardization issue by the adoption of model exchange standards like CellML, BioBrick standard biological parts and standard signal carriers for modeling purpose. The BioBricks are easily assemblable [1] standard DNA sequences coding for well-defined structures and functions and represent an effort to introduce the engineering principles of abstraction and standardization in synthetic biology. Web applications as GenoCAD [2] are available and implements an algorithm of syntax check of the circuits designed [3], while some other tools for automatic design and optimization of genetic circuits have appeared [4] and are also specific for BioBrick systems [5]. Our generated models are made of Standard Virtual Parts modular components. Model complexity includes more interaction dynamics than previous works. The inherent software complexity has been handled by a rational use of ontologies and rule engine. The database of parts and interactions is automatically created from publicly available whole system models. We implemented a genetic algorithm searching the space of possible genetic circuits for an optimal circuit meeting user defined input-output dynamics. The tools performing structural optimization usually use stochastic strategies, while those optimizing the parameters or matching the components for a given structure can take advantage of both stochastic and deterministic strategies. In most cases it is however necessary human intervention, for example to set the value of certain kinetic parameters. To our best knowledge no tool exists which does not show a couple of these limitations, then our tool is the only capable of using a library of parts, dynamically generated from other system models available from public databases [6]. The tool automatically infers the chemical and genetic interactions occurring between entities of the repository models and applies them in the target model if opportune. The repository models have to be modeled by a specific CellML standard, the Standard Virtual Parts (SVP) [7] formalism and the components have to be annotated with OWL for unique identifiers. The output is a sequence of readily composable biological components, deposited in the registry of parts, and a complete CellML kinetic model of the system. Accordingly, a model can be generated and simulated from a sequence of BioBrick, without any human intervention. Actual tools present a moderated degree of accuracy in the prediction of the behavior, principally due to the lack of consideration of many cellular factors. Despite the advances in molecular construction, modeling and fine-tuning the behavior of synthetic circuits remains extremely challenging [8]. We tried to cope with this issue of scalability by means of ontologies coupled with a rule engine [9]. Model complexity includes more interaction dynamics than previous works, including gene regulation, interaction between small molecules and proteins but also protein-protein and post-transcriptional regulation. The domain was described by using Ontology Web Language (OWL) ontologies in conjunction with CellML [10], while complex logic was added by Jess rules [11]. The system has been successfully tested on a single test case and looks towards the creation of a web platform [12].
Artificial IntelligenceVitoantonio Bevilacqua, Paolo Pannarale
A semantic expert system for the evolutionary design of synthetic gene networks
Vitoantonio Bevilacqua, Paolo Pannarale
Artificial Intelligence
05Aug2015
Artificial neural networks for feedback control of a human elbow hydraulic prosthesis.
Neurocomputing
The paper addresses feedback control of actuated prostheses based on the Stewart platform parallel mechanism. In such a problem it is essential to apply a feasible numerical method to determine in real time the solution of the forward kinematics, which is highly nonlinear and characterized by analytical indetermination. In this paper, the forward kinematics problem for a human elbow hydraulic prosthesis developed by the research group of Polytechnic of Bari is solved using artificial neural networks as an effective and simple method to obtain in real time the solution of the problem while limiting the computational effort. We show the effectiveness of the technique by designing a PID controller that governs the arm motion thanks to the provided neural computation of the forward kinematics.
Artificial IntelligenceVitoantonio Bevilacqua, Mariagrazia Dotoli, Mario Massimo Foglia, Francesco Acciani, Giacomo Tattoli, Marcello Valori
Artificial neural networks for feedback control of a human elbow hydraulic prosthesis.
Vitoantonio Bevilacqua, Mariagrazia Dotoli, Mario Massimo Foglia, Francesco Acciani, Giacomo Tattoli, Marcello Valori
Artificial Intelligence
09Dec2014
EasyCluster2: an improved tool for clustering and assembling long transcriptome reads.
BMC Bioinformatics
Expressed sequences (e.g. ESTs) are a strong source of evidence to improve gene structures and predict reliable alternative splicing events. When a genome assembly is available, ESTs are suitable to generate gene-oriented clusters through the well-established EasyCluster software. Nowadays, EST-like sequences can be massively produced using Next Generation Sequencing (NGS) technologies. In order to handle genome-scale transcriptome data, we present here EasyCluster2, a reimplementation of EasyCluster able to speed up the creation of gene-oriented clusters and facilitate downstream analyses as the assembly of full-length transcripts and the detection of splicing isoforms.
Design and Development of a Forearm Rehabilitation System Based on an Augmented Reality Serious Game
WIVACE 2015
In this paper, we propose a forearm rehabilitation system based on a serious game in Augmented Reality (AR). We designed and developed a simplified AR arcade brick breaking game to induce rehabilitation of the forearm muscles. We record the electromyographic signals using a low cost device to evaluate the applied force. We collected and analysed data in order to find a relationship between the applied force and the difficulty of the game. This research focuses on the de-hospitalization of subjects in the middle or final stages of their rehabilitation where the new technologies, like Virtual and Augmented Reality, may improve the experience of repetitive exercises.
The results achieved prove that the force applied by the user to hit the virtual sphere with real cardboard cube is related to sphere speed. In a rehabilitation scenario the results could be used to evaluate the improvements analysing the performance history.
Augmented and Virtual RealityVitoantonio Bevilacqua, Antonio Brunetti, Giuseppe Trigiante, Gianpaolo Francesco Trotta, Michele Fiorentino, Vito Manghisi, Antonio Emmanuele Uva
Design and Development of a Forearm Rehabilitation System Based on an Augmented Reality Serious Game
Vitoantonio Bevilacqua, Antonio Brunetti, Giuseppe Trigiante, Gianpaolo Francesco Trotta, Michele Fiorentino, Vito Manghisi, Antonio Emmanuele Uva
Augmented and Virtual Reality
02Apr2016
Optimizing Feed-Forward Neural Network Topology by Multi-objective Evolutionary Algorithms: A Comparative Study on Biomedical Datasets
WIVACE 2015
The design of robust classifiers, for instance Artificial Neural Networks (ANNs), is a critical aspect in all complex pattern recognition or classification tasks. Poor design choices may undermine the ability of the system to correctly classify the data samples. In this context, evolutionary techniques have proven particularly successful in exploring the complex state-space underlying the design of ANNs. Here, we report an extensive comparative study on the application of several modern Multi-Objective Evolutionary Algorithms to the design and training of an ANN for the classification of samples from two different biomedical datasets. Numerical results show that different algorithms have different strengths and weaknesses, leading to ANNs characterized by different levels of classification accuracy and network complexity.
Artificial IntelligenceVitoantonio Bevilacqua, Fabio Cassano, Ernesto Mininno, Giovanni Iacca
Optimizing Feed-Forward Neural Network Topology by Multi-objective Evolutionary Algorithms: A Comparative Study on Biomedical Datasets
Vitoantonio Bevilacqua, Fabio Cassano, Ernesto Mininno, Giovanni Iacca
Artificial Intelligence
15Sep2015
A Multimodal System for Nonverbal Human Feature Recognition in Emotional Framework.
EMPIRE@RecSys 2015
A correct recognition of nonverbal expressions is currently one of the most important challenges of research in the field of human computer interaction. The ability to recognize human actions could change the way to interact with machines in several environments and contexts, or even the way to live. In this paper, we describe the advances of a previous study finalized to design, implement and validate an innovative recognition system already developed by some of the authors. It was aimed at recognizing two opposite emotional conditions (resonance and dissonance) of a candidate to a job position interacting with the recruiter during a job interview. Results in terms of the accuracy, resonance rate, and dissonance rate of the three new optimized neural network-based (NN) classifiers are discussed. Comparison with previous results of three NN classifiers is also presented based on three single domains: facial, vocal and gestural.
Artificial IntelligenceData MiningVitoantonio Bevilacqua, Leonarda Carnimeo, Pietro Guccione, Giuseppe Mastronardi, Antonio Emmanuele Uva, Michele Fiorentino, Giuseppe Monno, Francescomaria Marino, Mariagrazia Dotoli, Nicola Costantino, Michele Dassisti, Nunzia Carbonara
A Multimodal System for Nonverbal Human Feature Recognition in Emotional Framework.
Vitoantonio Bevilacqua, Leonarda Carnimeo, Pietro Guccione, Giuseppe Mastronardi, Antonio Emmanuele Uva, Michele Fiorentino, Giuseppe Monno, Francescomaria Marino, Mariagrazia Dotoli, Nicola Costantino, Michele Dassisti, Nunzia Carbonara
Artificial IntelligenceData Mining
08May2015
An innovative framework for rare neurodegenerative diseases analysis
MeMeA 2015
In this paper we introduce a new non-invasive, inexpensive and functional tool in order to support physicians and specialists during visits of patients who are affected by neurodegenerative diseases, as well as Huntington's one. The proposed framework succeeds in automatizing International Affective Picture System (IAPS) test which consists of two phases: the first allows to show a sub set of pictures stored in a database, while the second takes note of Self Assessment Manikin scale evaluations; during the session, the patient's face is captured by a webcam for extracting emotional features for each frame. This framework, therefore, saves data in a SQLite Database. The proposed framework provides a medical efficient evaluation to evaluate the emotional impairment in these patients by comparing the results given by facial expressions analyzer and those given by the leading layer themselves in a long observation period.
Advanced classification of Alzheimer’s disease and healthy subjects based on EEG markers.
IJCNN 2015
In this study, we compared several classifiers for the supervised distinction between normal elderly and Alzheimer's disease individuals, based on resting state electroencephalographic markers, age, gender and education. Three main preliminary procedures served to perform features dimensionality reduction were used and discussed: a Support Vector Machines Recursive Features Elimination, a Principal Component Analysis and a novel method based on the correlation. In particular, five different classifiers were compared: two different configurations of SVM and three different optimal topologies of Error Back Propagation Multi Layer Perceptron Artificial Neural Networks (EBP MLP ANNs). Best result, in terms of classification (accuracy 86% and sensitivity 92%), was obtained by a Neural Network with 3 hidden layers that used as input: age, gender, education and 20 EEG features selected by the novel method based on the correlation.
Artificial IntelligenceVitoantonio Bevilacqua, Angelo Antonio Salatino, Carlo Di Leo, Giacomo Tattoli, Domenico Buongiorno, Domenico Signorile, Claudio Babiloni, Claudio Del Percio, Antonio Ivano Triggiani, Loreto Gesualdo
Advanced classification of Alzheimer’s disease and healthy subjects based on EEG markers.
Vitoantonio Bevilacqua, Angelo Antonio Salatino, Carlo Di Leo, Giacomo Tattoli, Domenico Buongiorno, Domenico Signorile, Claudio Babiloni, Claudio Del Percio, Antonio Ivano Triggiani, Loreto Gesualdo
Artificial Intelligence
12Jul2015
A supervised CAD to support telemedicine in hematology
IJCNN 2015
This paper presents the design and the implementation of a Computer Aided Diagnosis (CAD) system for the clinical analysis of Peripheral Blood Smears (PBS also called Blood Film). The proposed system is able to count and classify the five types of leucocytes located in the tail of a PBS for computing the leukocyte formula. Image processing and segmentation techniques were used to extract 33 leucocyte's features (morphological, chromatic and texture-based). Only 7 features, selected by using the Information Gain Ranking algorithm of Weka platform, were used to evaluate the classification performance of two different classifiers: Back Propagation Neural Network (BPNN) and Decision Tree (DT). From the comparison between the two proposed approaches we can argue that the BPNN performed better than the DT on the validation set. Finally, the Neural Network classifier was evaluated with a test set composed of 1274 leucocytes obtaining good results in terms of Precision (87.9%) and Sensitivity (97.4%).
Artificial IntelligenceComputer VisionMedical CADVitoantonio Bevilacqua, Domenico Buongiorno, Pierluigi Carlucci, Ferdinando Giglio, Giacomo Tattoli, Attilio Guarini, Nicola Sgherza, Giacoma De Tullio, Carla Minoia, Anna Scattone, Giovanni Simone, Francesco Girardi, Alfredo Zito, Loreto Gesualdo
A supervised CAD to support telemedicine in hematology
Vitoantonio Bevilacqua, Domenico Buongiorno, Pierluigi Carlucci, Ferdinando Giglio, Giacomo Tattoli, Attilio Guarini, Nicola Sgherza, Giacoma De Tullio, Carla Minoia, Anna Scattone, Giovanni Simone, Francesco Girardi, Alfredo Zito, Loreto Gesualdo
Artificial IntelligenceComputer VisionMedical CAD
13Aug2015
Neural Network Classification of Blood Vessels and Tubules Based on Haralick Features Evaluated in Histological Images of Kidney Biopsy
LNCS
In this paper, we present a Computer Aided Diagnosis that implements a supervised approach to discriminate vessels versus tubules that are two different types of structural elements in images of biopsy tissue. In particular, in this work we formerly describe an innovative preliminary step to segment region of interest, then the procedure to extract from them significant features and finally present and discuss the Back Propagation Neural Network binary classifier performance that shows Precision 91 % and Recall 91 %.
Artificial IntelligenceComputer VisionVitoantonio Bevilacqua, Nicola Pietroleonardo, Vito Triggiani, Loreto Gesualdo, Annamaria Di Palma, Michele Rossini, Giuseppe Dalfino, Nico Mastrofilippo
Neural Network Classification of Blood Vessels and Tubules Based on Haralick Features Evaluated in Histological Images of Kidney Biopsy
Vitoantonio Bevilacqua, Nicola Pietroleonardo, Vito Triggiani, Loreto Gesualdo, Annamaria Di Palma, Michele Rossini, Giuseppe Dalfino, Nico Mastrofilippo
Artificial IntelligenceComputer Vision
13Aug2015
A Computer Vision Method for the Italian Finger Spelling Recognition
LNCS
Sign Language Recognition opens to a wide research field with the aim of solving problems for the integration of deaf people in society. The goal of this research is to reduce the communication gap between hearing impaired users and other subjects, building an educational system for hearing impaired children. This project uses computer vision and machine learning algorithms to reach this objective. In this paper we analyze the image processing techniques for detecting hand gestures in video and we compare two approaches based on machine learning to achieve gesture recognition.
Artificial IntelligenceComputer VisionVitoantonio Bevilacqua, Luigi Biasi, Antonio Pepe, Giuseppe Mastronardi, Nicholas Caporusso
A Computer Vision Method for the Italian Finger Spelling Recognition
Vitoantonio Bevilacqua, Luigi Biasi, Antonio Pepe, Giuseppe Mastronardi, Nicholas Caporusso
Artificial IntelligenceComputer Vision
11Aug2015
A P300 Clustering of Mild Cognitive Impairment Patients Stimulated in an Immersive Virtual Reality Scenario
ICIC 2015
In this paper, we present an innovative framework useful for clustering patients affected by a mild cognitive impairment and designed to improve the living environment and the lifestyle of patients in order to delay their cognitive state decline. The cognitive state changes are evaluated by means the event - related potentials elicited by environmental stimuli administered in several Virtual Reality scenarios. In particular, we formerly describe our innovative Virtual Reality environment, the protocol of stimuli administration, the procedure to measure the P300 latency in the response signal and finally the Self Organizing Map used to cluster the data. This research finds application in the fields of re-qualification of the environments for patients and healthcare introducing a new method for evaluation of best living conditions through VR.
Artificial IntelligenceAugmented and Virtual RealityVitoantonio Bevilacqua, Antonio Brunetti, Davide de Biase, Giacomo Tattoli, Rosario Santoro, Gianpaolo Francesco Trotta, Fabio Cassano, Michele Pantaleo, Giuseppe Mastronardi, Fabio Ivona, Marianna Delussi, Anna Montemurno, Katia Ricci, Marina de Tommaso
A P300 Clustering of Mild Cognitive Impairment Patients Stimulated in an Immersive Virtual Reality Scenario
Vitoantonio Bevilacqua, Antonio Brunetti, Davide de Biase, Giacomo Tattoli, Rosario Santoro, Gianpaolo Francesco Trotta, Fabio Cassano, Michele Pantaleo, Giuseppe Mastronardi, Fabio Ivona, Marianna Delussi, Anna Montemurno, Katia Ricci, Marina de Tommaso
Artificial IntelligenceAugmented and Virtual Reality
06Jun2015
A neuromusculoskeletal model of the human upper limb for a myoelectric exoskeleton control using a reduced number of muscles.
WHC 2015
Abstract:
This paper presents a myoelectric control of an arm exoskeleton designed for rehabilitation. A four-muscles-based NeuroMusculoSkeletal (NMS) model was implemented and optimized using genetic algorithms to adapt the model to different subjects. The NMS model is able to predict the shoulder and elbow torques which are used by the control algorithm to ensure a minimal force of interaction. The accuracy of the method is assessed through validation experiments conducted with two healthy subjects performing free movements along the pseudo-sagittal plane. The experiments show promising results for our approach showing its potential for being introduced in a rehabilitation protocol.
Analysis and optimization of the 13C octanoic acid breath test
IJCNN 2017
Abstract:
Objectives: Nowadays breath test, using the Ghoss method for the calculation of gastric emptying of solids, is characterized by a very high number of expirations in a very long time (about 4 hours). In this work, a simplified model aiming to the reduction of the number of expirations during this time was designed, preserving high levels of accuracy, sensitivity and specificity in the classification between ‘normal’ or ‘delayed’ gastric emptying. Materials and Methods: Materials consist of 238 breath test exams from 66 different patients; for each exam, the relevance of each expiration was evaluated. Several models were designed and tested comparing their performance with a full model which took into account 17 expirations; among them, the model with the highest accuracy was selected: it consists of 7 expirations (baseline, 75, 135, 195, 210, 225 and 240 min). Results: Considering the previous model, the number of expirations was reduced by 62.5 %, still reaching high levels of accuracy, sensitivity and specificity (about 90 %) comparable with the full model which showed an accuracy of 98 %. Conclusion: The adoption of the proposed model led to a considerable reduction of required expirations, still having good performance in classifying ‘normal’ and ‘delayed’ gastric emptying. At the same time, since it requires fewer breaths, test was also simplified, allowing patients to do this exam at home. Furthermore, the reduction of breaths led to a considerable cost reduction of the entire examination.
Artificial IntelligenceVitoantonio Bevilacqua, Marco Riezzo, Antonio Brunetti, Francesco Russo, Benedetta D'Attoma, Giuseppe Riezzo
Analysis and optimization of the 13C octanoic acid breath test
Vitoantonio Bevilacqua, Marco Riezzo, Antonio Brunetti, Francesco Russo, Benedetta D'Attoma, Giuseppe Riezzo
Artificial Intelligence
20Jul2017
A Computer Aided Ophthalmic Diagnosis System Based on Tomographic Features.
ICIC 2017
Keratoconus is a bilateral progressive corneal disease characterized by thinning and apical protrusion; its early diagnosis is fundamental since it allows one to treat this rare disease by cross-linking approach, thus preventing a major corneal deformation and avoiding more invasive and risky surgical therapies, such as cornea transplant. Ophthalmology improvements have allowed a more rapid, precise and painless acquisition of corneal biometric parameters which are useful to evaluate alterations and abnormalities of eye’s outer structure. This paper presents a study about Keratoconus diagnosis based on a machine learning approach using corneal physical and morphological parameters obtained through Precisio™ tomographic examination. Artificial Neural Networks (ANNs) have been used for classification; in particular, a mono-objective Genetic Algorithm has been used to obtain the best topology for the neural classifiers for different input datasets obtained from features ranking. High levels of accuracy (higher than 90%) have been reached for all types of classification; in particular, binary classification has showed the best discrimination capability for Keratoconus identification.
Artificial IntelligenceComputer VisionMedical CADVitoantonio Bevilacqua, Sergio Simeone, Antonio Brunetti, Claudio Loconsole, Gianpaolo Francesco Trotta, Salvatore Tramacere, Antonio Argentieri, Francesco Ragni, Giuseppe Criscenti, Andrea Fornaro, Rosalina Mastronardi, Serena Cassetta, Giuseppe D'Ippolito
A Computer Aided Ophthalmic Diagnosis System Based on Tomographic Features.
Vitoantonio Bevilacqua, Sergio Simeone, Antonio Brunetti, Claudio Loconsole, Gianpaolo Francesco Trotta, Salvatore Tramacere, Antonio Argentieri, Francesco Ragni, Giuseppe Criscenti, Andrea Fornaro, Rosalina Mastronardi, Serena Cassetta, Giuseppe D'Ippolito
Artificial IntelligenceComputer VisionMedical CAD
20Jul2017
A Novel Approach in Combination of 3D Gait Analysis Data for Aiding Clinical Decision-Making in Patients with Parkinson’s Disease
ICIC 2017
The most common methods used by neurologist to evaluate Parkinson’s Disease (PD) patients are rating scales, that are affected by subjective and non-repeatable observations. Since several research studies have revealed that walking is a sensitive indicator
for the progression of PD. In this paper, we propose an innovative set of features derived from three-dimensional Gait Analysis in order to classify motor signs of motor impairment in PD and differentiate PD patients from healthy subjects or patients suffering from other neurological diseases. We consider kinematic data from Gait Analysis as Gait Variables Score (GVS), Gait Profile Score (GPS) and spatio-temporal data for all enrolled patients. We then carry out experiments evaluating the extracted features using an Artificial Neural Network (ANN) classifier. The obtained results are promising with the best classifier score accuracy equal to 95.05%.
Artificial IntelligenceIlaria Bortone, Gianpaolo Francesco Trotta, Antonio Brunetti, Giacomo Donato Cascarano, Claudio Loconsole, Nadia Agnello, Alberto Argentiero, Giuseppe Nicolardi, Antonio Frisoli, Vitoantonio Bevilacqua
A Novel Approach in Combination of 3D Gait Analysis Data for Aiding Clinical Decision-Making in Patients with Parkinson’s Disease
Ilaria Bortone, Gianpaolo Francesco Trotta, Antonio Brunetti, Giacomo Donato Cascarano, Claudio Loconsole, Nadia Agnello, Alberto Argentiero, Giuseppe Nicolardi, Antonio Frisoli, Vitoantonio Bevilacqua
Artificial Intelligence
20Jul2017
Computer Vision and EMG-Based Handwriting Analysis for Classification in Parkinson’s Disease
ICIC 2017
Handwriting analysis represents an important research area in different fields. From forensic science to graphology, the automatic dynamic and static analyses of handwriting tasks allow researchers to attribute the paternity of a signature to a specific person or to infer medical and psychological patients’ conditions. An emerging research field for exploiting handwriting analysis results is the one related to Neurodegenerative Diseases (NDs). Patients suffering from a ND are characterized by an abnormal handwriting activity since they have difficulties in motor coordination and a decline in cognition.
In this paper, we propose an approach for differentiating Parkinson’s disease patients from healthy subjects using a handwriting analysis tool based on a limited number of features extracted by means of both computer vision and ElectroMyoGraphy (EMG) signal-processing techniques and processed using an Artificial Neural Network-based classifier.
Finally, we report and discuss the results of an experimental test conducted with both healthy and Parkinson’s Disease patients using the proposed approach.
Artificial IntelligenceComputer VisionRehabilitationClaudio Loconsole, Gianpaolo Francesco Trotta, Antonio Brunetti, Joseph Trotta, Angelo Schiavone, Sabina Ilaria Tatò, Giacomo Losavio, Vitoantonio Bevilacqua
Computer Vision and EMG-Based Handwriting Analysis for Classification in Parkinson’s Disease
Claudio Loconsole, Gianpaolo Francesco Trotta, Antonio Brunetti, Joseph Trotta, Angelo Schiavone, Sabina Ilaria Tatò, Giacomo Losavio, Vitoantonio Bevilacqua
A Supervised Breast Lesion Images Classification from Tomosynthesis Technique
ICIC 2017
In this paper, we propose a deep learning approach for breast lesions classification, by processing breast images obtained using an innovative acquisition system, the Tomosynthesis, a medical instrument able to acquire high-resolution images using a lower radiographic dose than normal Computed Tomography (CT). The acquired images were processed to obtain Regions Of Interest (ROIs) containing lesions of different categories. Subsequently, several pre-trained Convolutional Neural Network (CNN) models were evaluated as feature extractors and coupled with non-neural classifiers for discriminate among the different categories of lesions. Results showed that the use of CNNs as feature extractor and the subsequent classification using a non-neural classifier reaches high values of Accuracy, Sensitivity and Specificity.
Artificial IntelligenceComputer VisionMedical CADVitoantonio Bevilacqua, Daniele Altini, Martino Bruni, Marco Riezzo, Antonio Brunetti, Claudio Loconsole, Andrea Guerriero, Gianpaolo Francesco Trotta, Rocco Fasano, Marica Di Pirchio, Cristina Tartaglia, Elena Ventrella, Michele Telegrafo, Marco Moschetta
A Supervised Breast Lesion Images Classification from Tomosynthesis Technique
Vitoantonio Bevilacqua, Daniele Altini, Martino Bruni, Marco Riezzo, Antonio Brunetti, Claudio Loconsole, Andrea Guerriero, Gianpaolo Francesco Trotta, Rocco Fasano, Marica Di Pirchio, Cristina Tartaglia, Elena Ventrella, Michele Telegrafo, Marco Moschetta
Artificial IntelligenceComputer VisionMedical CAD
01Jan2017
A novel approach for Hepatocellular Carcinoma detection and classification based on triphasic CT Protocol
CEC 2017
Subjects affected by chronic liver disease have higher probability to present Hepatocellular Carcinoma (HCC), the fifth most common cancer worldwide and the third most common cause of cancer mortality [1]. Since HCCs occur in the liver only, it is told as primary liver cancer; on the other side, there are also secondary liver cancers, which spread to the liver from other organs [2]. The underlying chronic liver disease, reaching the cirrotich stage, seems to be the cause in most of the HCC cases worldwide [3], [4]. Less common causes include Wilson's disease, hereditary hemochromatosis, alphal-antitrypsin deficiency, primary biliary cirrhosis and autoimmune hepatitis [5]. According to World Health Organization (WHO), an HCC can be analyzed from both macroscopic and microscopic point of views; in the first case, the degree of tumor depends on size and presence (or absence) of liver cirrhosis; in the second case, an histological classification of tumors includes trabecular (plate-like), pseudoglandular and acinar, compact and scirrhous types [6]. There are different ways in which hepatocellular carcinomas are classified. According to the Liver Cancer Study Group of Japan, the nodules associated with liver cirrhosis could be histologically divided into six categories: large regenerative nodules, adenomatous hyperplasia (AH), atypical AH, early HCC, well differentiated HCC, and moderately or poorly differentiated HCC (so-called classical HCC) [7], [8]. Another system used to stage HCCs is the Barcellona clinic liver cancer (BCLC) approach [9], [10]; it divides HCC into 4 categories (early, intermediate, advanced and terminal stages) based on different characteristics, such as ECOG performance status [11], Child-Pugh score [12], vascular invasion or nodal spread and extrahepatic metastases.
Computer VisionMedical CADVitoantonio Bevilacqua, Antonio Brunetti, Gianpaolo Francesco Trotta, Giovanni Dimauro, Katarina Elez, Vito Alberotanza, Arnaldo Scardapane
A novel approach for Hepatocellular Carcinoma detection and classification based on triphasic CT Protocol
Vitoantonio Bevilacqua, Antonio Brunetti, Gianpaolo Francesco Trotta, Giovanni Dimauro, Katarina Elez, Vito Alberotanza, Arnaldo Scardapane
Computer VisionMedical CAD
01Jan2016
Photogrammetric Meshes and 3D Points Cloud Reconstruction: A Genetic Algorithm Optimization Procedure.
Virtual reconstruction of heritage is one of the most interesting and innovative tool for preservation and keeping of historical, architectural and artistic memory of many sites that are in danger of disappearing. Find the best way to present an object in virtual reality is necessary for reasons linked to technology itself. In particular, the rendering of heavy object, in terms of details and meshes, influences the presentation of the whole virtual scene
Artificial IntelligenceAugmented and Virtual RealityComputer VisionVitoantonio Bevilacqua, Gianpaolo Francesco Trotta, Antonio Brunetti, Giuseppe Buonamassa, Martino Bruni, Giancarlo Delfine, Marco Riezzo, Michele Amodio, Giuseppe Bellantuono, Domenico Magaletti, Luca Verrino, Andrea Guerriero
Photogrammetric Meshes and 3D Points Cloud Reconstruction: A Genetic Algorithm Optimization Procedure.
Vitoantonio Bevilacqua, Gianpaolo Francesco Trotta, Antonio Brunetti, Giuseppe Buonamassa, Martino Bruni, Giancarlo Delfine, Marco Riezzo, Michele Amodio, Giuseppe Bellantuono, Domenico Magaletti, Luca Verrino, Andrea Guerriero
Artificial IntelligenceAugmented and Virtual RealityComputer Vision
15May2016
VoxTester, software for digital evaluation of speech changes in Parkinson disease.
MeMeA
Abstract:
Patients with Parkinson's disease may have difficulties in speaking because of the reduced coordination of the muscles that control breathing, phonation, articulation and prosody. Symptoms that may occur because of changes are weakening of the volume of the voice, voice monotony, changes in the quality of the voice, speed of speech, uncontrolled repetition of words.
Medical CADGiovanni Dimauro, Danilo Caivano, Vitoantonio Bevilacqua, Francesco Girardi, Vito Napoletano
VoxTester, software for digital evaluation of speech changes in Parkinson disease.
Giovanni Dimauro, Danilo Caivano, Vitoantonio Bevilacqua, Francesco Girardi, Vito Napoletano
Medical CAD
15May2016
A novel approach to evaluate blood parameters using computer vision techniques
MeMeA
Abstract:
Current medical practice for determining hemoglobin concentration (which is especially important for anemic patients in need of blood transfusion) involves frequent blood tests. In this work, we propose an alternative, non-invasive approach to hemoglobin estimation, based on image analysis of a specific conjunctival region.
Computer VisionMedical CADVitoantonio Bevilacqua, Giovanni Dimauro, Francescomaria Marino, Antonio Brunetti, Fabio Cassano, Antonio Di Maio, Enrico Nasca, Gianpaolo Francesco Trotta, Francesco Girardi, Angelo Ostuni, Attilio Guarini
A novel approach to evaluate blood parameters using computer vision techniques
Vitoantonio Bevilacqua, Giovanni Dimauro, Francescomaria Marino, Antonio Brunetti, Fabio Cassano, Antonio Di Maio, Enrico Nasca, Gianpaolo Francesco Trotta, Francesco Girardi, Angelo Ostuni, Attilio Guarini
Computer VisionMedical CAD
12Jul2016
Computer Assisted Detection of Breast Lesions in Magnetic Resonance Images
ICIC 2016: Intelligent Computing Theories and Application
Nowadays preventive screening policies and increased awareness initiatives are up surging the workload of radiologists. Due to the growing number of women undergoing first-level screening tests, systems that can make these operations faster and more effective are required. This paper presents a Computer Assisted Detection system based on medical imaging techniques and capable of labeling potentially cancerous breast lesions.
Artificial IntelligenceComputer VisionMedical CADVitoantonio Bevilacqua, Maurizio Triggiani, Maurizio Dimatteo, Giuseppe Bellantuono, Antonio Brunetti, Leonarda Carnimeo, Francescomaria Marino, Michele Telegrafo, Marco Moschetta
Computer Assisted Detection of Breast Lesions in Magnetic Resonance Images
Vitoantonio Bevilacqua, Maurizio Triggiani, Maurizio Dimatteo, Giuseppe Bellantuono, Antonio Brunetti, Leonarda Carnimeo, Francescomaria Marino, Michele Telegrafo, Marco Moschetta
Artificial IntelligenceComputer VisionMedical CAD
01Jan2016
A Dynamic Approach to Medical Data Visualization and Interaction.
VVH@AVI
Modern web applications lack in flexibility when multiple
medical data are shown at the same time. This could bring users to not
consider important aspects of their health status and physicians to lose
critical patients situations. The ”Registro Elettronico Sanitario Personale”
(RESP, in English ”Personal Sanitary Electronic Registry”) is a prototype
of a web portal allowing patients and physicians to share health
problems, diagnosis, prognosis, pharmaceutical therapies etc
Data MiningVitoantonio Bevilacqua, Fabio Cassano, Giovanni Dimauro, Francesco Girardi, Antonio Piccinno
A Dynamic Approach to Medical Data Visualization and Interaction.
Vitoantonio Bevilacqua, Fabio Cassano, Giovanni Dimauro, Francesco Girardi, Antonio Piccinno
Data Mining
12Jul2016
Adaptive Bi-objective Genetic Programming for Data-Driven System Modeling.
ICIC 2016: Intelligent Computing Methodologies
We propose in this paper a modification of one of the modern state-of-the-art genetic programming algorithms used for data-driven modeling, namely the Bi-objective Genetic Programming (BioGP). The original method is based on a concurrent minimization of both the training error and complexity of multiple candidate models encoded as Genetic Programming trees.
Artificial IntelligenceData MiningVitoantonio Bevilacqua, Nicola Nuzzolese, Ernesto Mininno, Giovanni Iacca
Adaptive Bi-objective Genetic Programming for Data-Driven System Modeling.
Vitoantonio Bevilacqua, Nicola Nuzzolese, Ernesto Mininno, Giovanni Iacca
Artificial IntelligenceData Mining
12Jul2016
A Computer Vision and Control Algorithm to Follow a Human Target in a Generic Environment Using a Drone
ICIC 2016: Intelligent Computing Methodologies
This work proposes an innovative technique to solve the problem of tracking and following a generic human target by a drone in a natural, possibly dark scene. The algorithm does not rely on color information but mainly on shape information, using the HOG classifier, and on local brightness information, using the optical flow algorithm.
Computer VisionVitoantonio Bevilacqua, Antonio Di Maio:
A Computer Vision and Control Algorithm to Follow a Human Target in a Generic Environment Using a Drone
Vitoantonio Bevilacqua, Antonio Di Maio:
Computer Vision
02Jul2016
A Linear Optimization Procedure for an EMG-driven NeuroMusculoSkeletal Model Parameters Adjusting: Validation Through a Myoelectric Exoskeleton Control
EuroHaptics
This paper presents a linear optimization procedure able to adapt a simplified EMG-driven NeuroMusculoSkeletal (NMS) model to the specific subject. The optimization procedure could be used to adjust a NMS model of a generic human articulation in order to predict the joint torque by using ElectroMyoGraphic (EMG) signals. The proposed approach was tested by modeling the human elbow joint with only two muscles.
RehabilitationRoboticsDomenico Buongiorno, Francesco Barone, Massimiliano Solazzi, Vitoantonio Bevilacqua, Antonio Frisoli
A Linear Optimization Procedure for an EMG-driven NeuroMusculoSkeletal Model Parameters Adjusting: Validation Through a Myoelectric Exoskeleton Control
Domenico Buongiorno, Francesco Barone, Massimiliano Solazzi, Vitoantonio Bevilacqua, Antonio Frisoli
RehabilitationRobotics
20Jul2016
An Optimized Feed-forward Artificial Neural Network Topology to Support Radiologists in Breast Lesions Classification.
GECCO (Companion)
Computer Aided Decision (CAD) systems based on Medical Imaging could support radiologists in classifying malignant regions from benign ones, in the field of investigation for breast cancer detection. This decision may often follow a previous procedure dedicated to the earlier identification of Regions Of Interest (ROI) containing still unclassified lesions
Artificial IntelligenceComputer VisionMedical CADVitoantonio Bevilacqua, Antonio Brunetti, Maurizio Triggiani, Domenico Magaletti, Michele Telegrafo, Marco Moschetta:
An Optimized Feed-forward Artificial Neural Network Topology to Support Radiologists in Breast Lesions Classification.
Vitoantonio Bevilacqua, Antonio Brunetti, Maurizio Triggiani, Domenico Magaletti, Michele Telegrafo, Marco Moschetta:
Artificial IntelligenceComputer VisionMedical CAD
11Jun2016
Design of a Projective AR Workbench for Manual Working Stations.
AVR
We present the design and a prototype of a projective AR workbench for an effective use of the AR in industrial applications, in particular for Manual Working Stations. The proposed solution consists of an aluminum structure that holds a projector and a camera that is intended to be mounted on manual working stations. The camera, using a tracking algorithm, computes in real time the position and orientation of the object while the projector displays the information always in the desired position.
Augmented and Virtual RealityComputer VisionAntonio Emmanuele Uva, Michele Fiorentino, Michele Gattullo, Marco Colaprico, Maria F. de Ruvo, Francescomaria Marino, Gianpaolo Francesco Trotta, Vito M. Manghisi, Antonio Boccaccio, Vitoantonio Bevilacqua, Giuseppe Monno:
Design of a Projective AR Workbench for Manual Working Stations.
Antonio Emmanuele Uva, Michele Fiorentino, Michele Gattullo, Marco Colaprico, Maria F. de Ruvo, Francescomaria Marino, Gianpaolo Francesco Trotta, Vito M. Manghisi, Antonio Boccaccio, Vitoantonio Bevilacqua, Giuseppe Monno:
Augmented and Virtual RealityComputer Vision
16Oct2016
Special issue on Advanced Intelligent Computing Methodologies and Applications.
Special issue on Advanced Intelligent Computing Methodologies and Applications.
Lin Zhu, Vitoantonio Bevilacqua, De-Shuang Huang
Artificial Intelligence
08Mar2017
An innovative neural network framework to classify blood vessels and tubules based on Haralick features evaluated in histological images of kidney biopsy.
Neurocomputing
Introduction and objective: Computer Aided Diagnosis (CAD) systems based on Medical Imaging could support physicians in several fields and recently are also applied in histopathology. The aim of this work is to discuss in detail the design and testing of a CAD system for segmentation and discrimination of blood vessels versus tubules from biopsies in the kidney tissue through the elaboration of histological images.
Artificial IntelligenceMedical CADVitoantonio Bevilacqua, Nicola Pietroleonardo, Vito Triggiani, Antonio Brunetti, Annamaria Di Palma, Michele Rossini, Loreto Gesualdo
An innovative neural network framework to classify blood vessels and tubules based on Haralick features evaluated in histological images of kidney biopsy.
Vitoantonio Bevilacqua, Nicola Pietroleonardo, Vito Triggiani, Antonio Brunetti, Annamaria Di Palma, Michele Rossini, Loreto Gesualdo
Artificial IntelligenceMedical CAD
.04
RESEARCH
LABORATORY TEAM
Ing. Antonio Brunetti
Ph.D. Student - Collaborator on PRECIOUS project
Ing. Gianpaolo Francesco Trotta
Ph.D. Student - Collaborator on PRECIOUS project
Dott. Maurizio Triggiani
Collaborator on PRECIOUS project
Dott. Irio De Feudis
Collaborator on PRECIOUS project
Dott.ssa Roberta Mascetti
Collaborator on FIT and GCESYS projects
Dott. Vito Manghisi
Ph.D. Student - Fellow Reasearcher FIT GCESYS
Other collaborators in research projects
Prof. Ing. Andrea Guerriero
Professor
Prof. Ing. Leonarda Carnimeo
Professor
Prof. Ing. Pietro Guccione
Professor
Dott. Nicholas Caporusso
Fellow Reasearcher at University of Salford formerly collaborator on PRECIOUS project
Ing. Domenico Buongiorno
PhD Student
RESEARCH PROJECTS
PRE.C.I.O.U.S.
PREdictive Computer aIded scOring sUpport System
PRE.C.I.O.U.S. (PREditive Computer aIded scOring sUpport System) aims to develop an innovative technology platform that can monitor the patient with sepsis, heart failure and myocardial infarction, predict the development of acute renal failure, and suggest personalized therapeutic / assistance pathways; Applying a new monitoring methodology that creates a new predictive model that can significantly reduce the occurrence of adverse events. The main monitored clinical conditions of the PRE.C.I.O.U.S project are the AKI and the SEPSI using MEWS indices. Below are details of these conditions.
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RESCAP
Living Lab Regione Puglia
The Rescap project aims to improve the quality of life of people suffering from psychological and / or physical deficits through the upgrading of their daily living environments.
This upgrading follows the requirements identified by a search environment configuration phase that will induce the lesser amount of stress on the individual living them. Through a virtualization phase of these environments, the patient is immersed in a virtual replica of his daily environment, within which it is possible to vary in real time some of the features, real-time observation of the subject's reactions and measuring the amount of stress perceived.
Determination of the amount of stress is obtained through the recognition of precise patterns in instruments produced by instruments such as electroencephalogram (EEG), electrocardiogram (ECG) and other sensors such as Sympathetic Skin Response (SSR) and thoracic respiration.
Once the most suitable configuration of the environments is identified, it is implemented using advanced home automation technologies, accessible by the patient through tactile or voice interfaces.
When implemented, when a new contact environment is used, a contact center will monitor the clinical and emotional situation of the patient remotely and a team of experts will evaluate in particular cases whether to make changes or not to the behavior of the home automation system.
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.05
TEACHING
HUMAN COMPUTER INTERACTION
Polytechnic University of Bari
NOW2008
MEDICAL INFORMATICS
Polytechnic University of Bari
Degree thesis and internship
Academic thesis
At the current date, topics are available to complete the thesis in:
Fundamentals of Computer Science II (Three Year Computer Science in Computer Science and Automation)
Fundamentals of Computer Science (three-year CdL in Managing and Electrical Office of Foggia)
Elements of Expert Systems (three-year CdL in Management, Electrical, Computer and Automation and CdL in Electrical Engineering, Electrical Engineering, Computer Science and Automation)
Medical Informatics (three-year CdL in Computer Science and Automation and CdL specialist in Electronics, Computer Science and Automation)
Image Processing and Artificial Vision (CdL specialist and master in Computer Engineering and Automation Engineering)
Man-Machine Interaction (Master's Degree in Computer Engineering and Automation Engineering)
Industrial Thesis
All theses can be done as a rapporteur or co-author in collaboration with other academic or business experts:
Bioinformatics and Systems Biology Ph.D. Filippo Menolascina Massachusetts Institute of Technology - Cambridge - Boston
Image Processing and Artificial Vision
Medical Imaging to Support Diagnosis, Prognosis and Therapy: Tumor Institute John Paul II of Bari U.O. Radiology, Radiotherapy and Physical Health
Diagnostics for Images - University Hospital of Bari - Prof.ii Rubini - Niccoli Asabella - Moschetta (Radiology and Nuclear Medicine)
Virtual Reality Increased - CETMA Brindisi (Computer Engineering only) also with the collaboration of Prof. Uva e Fiorentino
Robotics and Artificial Vision - CETMA Brindisi (only for Electronic Engineering and Automation) also with the collaboration of Prof. Rizzo e Foglia
Image Diagnostics - Prof. Bellotti National Institute of Nuclear Physics
Image Processing and Artificial Vision for Cultural Heritage - Prof. Giuliano De Felice - University of Foggia www.archeologiadigitale.it
Bioinformatics - Lab Washers http://www.pesolelab.it/ - Proff. Pesoles and Picardi
Bioinformatics - Tumor Institute John Paul II - Bari - Laboratory of Bioinformatics (eg Paradiso, Tommasi, Bevilacqua)
Data Mining and Expert Systems for Economics and Business Organization - Prof. Di Nauta e Pacelli - University of Foggia
Soft Computing for Automation - Prof. Dotoli
Chemistry Decision Support Systems - Prof. Gallo
Data Mining and Expert Systems for Health and Medicine - Exprivia - AMT services - Agilex
Computing for Automation - Ing. Piero Larizza - Masmec
Virtual Reality Increased - Prof. Uva and Fiorentino - DMMM Politecncio of Bari
Signals and Voice Commands - Prof. Mastronardi and Guccione - Of the Politecnico di Bari
Intelligent Domotics - eBIS and AMT services
Man-Machine Interaction - Percer's Workshop - PISA's Sant'Anna High School - Prof. Frisoli and Ingg. Loconsole and Leonardis.
Il progetto SKIN intende puntare sull’innovazione del prodotto editoriale realizzata da coloro che insieme rappresentano tutti gli anelli della filiera produttiva. A tal fine si sono individuate delle linee di azione che costituiscono un ampliamento dell’offerta di prodotti e servizi utili al mondo dell’editoria:
Implementazione di procedure che consentano alle aziende editoriali il recupero dei contenuti da prodotti originariamente e prevalentemente destinati alla stampa, la loro suddivisione in contenuti elementari atomici semanticamente definiti, la loro catalogazione tipologica e la loro archiviazione in una base di dati con possibilità di revisione/integrazione/elaborazione di nuovi contenuti.
Ideazione e implementazione di prototipi di prodotti editoriali che utilizzino contenuti proprietari atomizzati, ne consentano la valorizzazione e risultino flessibili e facilmente adattabili al target di destinazione (editori o utenti finali).
Progettazione e implementazione di una innovativa piattaforma di ricerca, aggregazione e delivery di contenuti, accessibile sia da web che da dispositivi mobile, realizzando un aggregatore personalizzato di risorse e un sistema di fruizione e delivery sia per singole unità che per unità complesse.
AMT Services Projects
Rescap
Rescap
About The Project
Il poster di Rescap presentato ad INISTA 2014
INTRODUZIONE AL PROGETTO
Il progetto Rescap si prefigge l’obiettivo di migliorare la qualità della vita di soggetti affetti da deficit psicologici e/o fisici attraverso la riqualificazione dei loro ambienti vitali quotidiani.
Questa riqualificazione segue dei requisiti individuati da una fase di ricerca della configurazione degli ambienti tale da indurre la quantità minore di stress sull’individuo che li vive. Attraverso una fase di virtualizzazionedi tali ambienti, il paziente viene immerso in una replica virtuale dei suoi ambienti quotidiani, all’interno della quale è possibile variare in tempo reale alcune caratteristiche, osservando in tempo reale le reazioni del soggetto e misurandone la quantità di stress percepita.
La determinazione della quantità di stress è ottenuta attraverso il riconoscimento di pattern ben precisi nei tracciati prodotti da strumenti come l’elettroencefalogramma (EEG), elettrocardiogramma (ECG) ed altri sensori come SSR (Sympathetic Skin Response) e di respirazione toracica.
Individuata la configurazione più adatta degli ambienti, la si implementa servendosi di tecnologie domotiche avanzate, accessibili da parte del paziente mediante interfacce tattili o vocali.
Ad implementazione completata, durante la fruizione dei nuovi ambienti, un contact center terrà sotto controllo remoto la situazione clinica ed emotica del paziente e un team di esperti valuterà in casi particolari se apportare modifiche o meno al comportamento del sistema domotico.
Il Laboratorio di Informatica Industriale, rappresentante il Politecnico di Bari, partecipa al progetto Rescap come laboratorio di ricerca a supporto degli altri partner, fornendo skills negli ambiti della realtà virtuale, acquisizione, elaborazione delle immagini e visione artificiale in generale.
DESCRIZIONE DEL SISTEMA SVILUPPATO
Il sistema Rescap si propone di individuare la configurazione con la minore quantità di stress intervenendo sulle seguenti caratteristiche degli ambienti:
Scuri automatici delle finestre;
Variazione del colore dell’illuminazione delle stanze;
Variazione colore delle pareti delle stanze attraverso strip led;
Riproduzione e diffusione di voci familiari per il paziente;
Riproduzione e diffusione suoni ambientali negli ambienti;
Percorsi luminosi di colore diverso a seconda della stanza verso cui conducono, da installare sul pavimento.
RICONOSCIMENTI
Premio Sociale IEEE International Symposium on INnovations in Intelligent SysTems and Applications (23-25 Giugno 2014, Alberobello), per il maggiore gradimento sulla piattaforma dei Living Labs.
DIFFUSIONE RISULTATI E PUBBLICAZIONI
I risultati verranno diffusi in accordo alla procedure tipiche di un Living Lab, ovvero tramite laboratori aperti ai cittadini, i quali partecipano attivamente alla discussione dei requisiti e delle caratteristiche del progetto, sperimentando in prima persona quanto sviluppato.
Programma televisivo Nautilus – 11 febbraio 2015 – canale RaiScuola
Smart Innovation People Gallery – “Innovactors” Bari, 29 September 2014
BIBLIOGRAFIA
Vitoantonio Bevilacqua, Antonio Brunetti, Davide de Biase, Giacomo Tattoli, Rosario Santoro, Gianpaolo Francesco Trotta, Fabio Cassano, Michele Pantaleo, Giuseppe Mastronardi, Fabio Ivona, Marianna Delussi, Anna Montemurno, Katia Ricci, Marina de Tommaso: “A P300 Clustering of Mild Cognitive Impairment Patients Stimulated in an Immersive Virtual Reality Scenario”, ICIC 2015
AMT Services Projects
IHCS – Innovative Health Care System
IHCS – Innovative Health Care System
About The Project
IHCS ( Innovative Health Care System ) è un progetto finanziato dalla regione Puglia in ambito di un bando Living Lab, al quale hanno preso parte diverse aziende con l’obiettivo di sviluppare un sistema di telemedicina, atto a favorire la riabilitazione ed il monitoraggio costante di pazienti affetti da malattie rare neurodegenerative.
L’architettura logico – fisica del sistema è costituita da un nodo centrale ( portale web ) che ha il compito di tenere traccia di informazioni relative a quattro tipi di attori: pazienti, care giver , medici di base e\o specialisti.
In qualsiasi momento, ciascuno di questi attori è in grado di accedere ad una serie di informazioni ( opportunamente filtrate a seconda dei casi ) tramite il portale web.
E’ importante sottolineare che l’intero progetto è finalizzato a monitorare un determinato sottoinsieme di pazienti, ovvero coloro che sono affetti da malattie neurodegenerative, nella fattispecie la malattia di Huntington.
AMT Services Projects
SS-RR PON FIT
SS-RR PON FIT
About The Project
Il progetto di ricerca SS-RR (Sviluppo di un Sistema per la Rilevazione della Risonanza) si pone l’obiettivo di sviluppare delle metodologie che, supportate da tecnologie esistenti, possano dar vita a servizi innovativi. In particolare lo scenario in cui ci si muove è quello in cui vi è interesse a rilevare i livelli di armonia relativi ad uno o più individui in relazione ad una serie di eventi e stimoli esterni. Ci si riferisce, ad esempio, alla misurazione del gradiente di soddisfazione di un interlocutore, al gradiente di felicità di un soggetto intervistato, al gradiente di interesse di uno studente nel corso di una lezione, etc. Da questo punto di vista, l’innovazione e l’utilità portati dal programma potranno tradursi nella moderna volontà di conoscere la sintonia esistente tra le persone che interagiscono, nella misurazione della accresciuta conoscenza, preparazione o interesse maturati a valle di un confronto, di un dialogo, di una lezione, di un’indagine, di un’esperienza di gruppo. Il Tema del Programma ovviamente riguarda il Settore dell’ICT – Informatica (architettura e sistemi di elaborazione) e prevede lo sviluppo di applicazioni software in grado di girare su opportuni dispositivi hardware. Attualmente esistono dispositivi con alcune di tali funzionalità, si pensi per esempio alle cosiddette macchine della verità, ma essi risultano essere di fatto inutilizzabili in contesti reali e difficilmente accettabili dagli utenti a causa della loro invasività. Questo progetto ha l’obiettivo di utilizzare le tecnologie attualmente disponibili sul mercato per creare dei servizi a valore aggiunto. In particolare utilizzando dispositivi quali smart-phone, iPhone, palmari, portatili di ultima generazione è possibile pensare di utilizzare dei software associati ad opportuna sensoristica in grado di acquisire informazioni sullo stato emotivo di chi si sta monitorando. I dati a cui si fa riferimento sono quelli propri del corpo umano sottoposto a particolari sollecitazioni fisico-emotive. Dall’incrocio di questi dati sarà possibile, attraverso sofisticati modelli propri della psicologia, ricostruire il gradiente di soddisfazione di un interlocutore, il gradiente di felicità di un soggetto intervistato, il gradiente di interesse di uno studente nel corso di una lezione, etc. Come precedentemente argomentato, quindi, non sarà necessario utilizzare sofisticate apparecchiature invasive, ma unicamente sfruttare le possibilità fornite dai moderni dispositivi, l’introduzione di modelli derivanti dalla psicologia e in grado di legare parametri fisici-emozionali a stati della persona e l’utilizzo di software appositamente progettati ed implementati in grado di collezionare, attraverso l’utilizzo di semplici dispositivi non invasivi, simili all’iPod per tipologia e dimensione, i dati.
AMT Services Projects
MET-AAL
MET-AAL
About The Project
Il progetto Met-AAL (METhodology and instruments for pervasive model in Ambient Assisted Living) è stato finanziato dalla regione Puglia in ambito del bando: FESR “AIUTI A SOSTEGNO DEI PARTENARIATI REGIONALI PER L’INNOVAZIONE” (POR FESR 2007-2013 Obiettivo Convergenza – ASSE I – Linea 1.2 – Azione 1.2.4 “Investiamo nel vostro futuro”), al quale hanno preso parte diverse aziende pugliesi leader nel proprio settore. L’obiettivo è stato quello di definire e realizzare una piattaforma di ambient intelligence dotata di tecnologia pervasiva utile per fornire supporto, assistenza e servizi ai soggetti con carenze di autosufficienza.
In2Lab Projects
PRE.C.I.O.U.S.
PRE.C.I.O.U.S.
About The Project
PRE.C.I.O.U.S. (PREdictive Computer aIded scOring sUpport System) si propone di sviluppare una piattaforma tecnologica innovativa in grado di monitorare il paziente con sepsi, scompenso cardiaco e infarto del miocardio.
In2Lab Projects
e-SUAP
e-SUAP
About The Project
E-SUAP, the integrated platform for the telematic management of the SUAP (Single Activity Product Desk) aims to make the procedural way for opening or modifying productive activities in the territory more efficiently and effectively by interfacing with a single ” Place “both the competent offices and the end-users of instances, with the aim of reducing the time for obtaining permits and verifying their requirements in accordance with current rules.
