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Recently, the role of AI in the development of eHealth is becoming increasingly ambitious since AI is allowing the development of whole new healthcare areas. In many cases, AI offers the possibility to support patient screening and monitoring through low-cost, non-invasive tests. One of the most relevant sectors in which a great contribution from AI is expected is that of neurodegenerative diseases, which represent one of the most important pathologies in Western countries with very serious follow up not only clinical, but also social and economic. In this context, AI certainly represents an indispensable tool for effectively addressing aspects related to early diagnosis but also to monitoring patients suffering from various neurodegenerative diseases. To achieve these results, AI tools must be made available in test applications on mobile devices that are also easy to use by a large part of the population. In this sense, the aspects related to human-machine interaction are of paramount relevance for the diffusion of these solutions. This article presents a mobile device application based on artificial intelligence tools for the early diagnosis and monitoring of patients suffering from neurodegenerative diseases and illustrates the results of specific usability tests that highlight the strengths but also the limitations in the iteration with application users. Some concluding remarks are highlighted to face the actual limitations of the proposed solution.
Annamaria Demarinis Loiotile; Vincenzo Dentamaro; Paolo Giglio; Donato Impedovo. AI-Based Clinical Decision Support Tool on Mobile Devices for Neurodegenerative Diseases. Lecture Notes in Computer Science 2021, 139 -148.
AMA StyleAnnamaria Demarinis Loiotile, Vincenzo Dentamaro, Paolo Giglio, Donato Impedovo. AI-Based Clinical Decision Support Tool on Mobile Devices for Neurodegenerative Diseases. Lecture Notes in Computer Science. 2021; ():139-148.
Chicago/Turabian StyleAnnamaria Demarinis Loiotile; Vincenzo Dentamaro; Paolo Giglio; Donato Impedovo. 2021. "AI-Based Clinical Decision Support Tool on Mobile Devices for Neurodegenerative Diseases." Lecture Notes in Computer Science , no. : 139-148.
Neurodegenerative disease assessment with handwriting has been shown to be effective. In this exploratory analysis, several features are extracted and tested on different tasks of the novel HAND-UNIBA dataset. Results show what are the most important kinematic features and the most significant tasks for neurodegenerative disease assessment through handwriting.
Vincenzo Dentamaro; Donato Impedovo; Giuseppe Pirlo. An Analysis of Tasks and Features for Neuro-Degenerative Disease Assessment by Handwriting. Transactions on Petri Nets and Other Models of Concurrency XV 2021, 536 -545.
AMA StyleVincenzo Dentamaro, Donato Impedovo, Giuseppe Pirlo. An Analysis of Tasks and Features for Neuro-Degenerative Disease Assessment by Handwriting. Transactions on Petri Nets and Other Models of Concurrency XV. 2021; ():536-545.
Chicago/Turabian StyleVincenzo Dentamaro; Donato Impedovo; Giuseppe Pirlo. 2021. "An Analysis of Tasks and Features for Neuro-Degenerative Disease Assessment by Handwriting." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 536-545.
Neurodegenerative diseases are particular diseases whose decline can partially or completely compromise the normal course of life of a human being. In order to increase the quality of patient’s life, a timely diagnosis plays a major role. The analysis of neurodegenerative diseases, and their stage, is also carried out by means of gait analysis. Performing early stage neurodegenerative disease assessment is still an open problem. In this paper, the focus is on modeling the human gait movement pattern by using the kinematic theory of rapid human movements and its sigma-lognormal model. The hypothesis is that the kinematic theory of rapid human movements, originally developed to describe handwriting patterns, and used in conjunction with other spatio-temporal features, can discriminate neurodegenerative diseases patterns, especially in early stages, while analyzing human gait with 2D cameras. The thesis empirically demonstrates its effectiveness in describing neurodegenerative patterns, when used in conjunction with state-of-the-art pose estimation and feature extraction techniques. The solution developed achieved 99.1% of accuracy using velocity-based, angle-based and sigma-lognormal features and left walk orientation.
Vincenzo Dentamaro; Donato Impedovo; Giuseppe Pirlo. Gait Analysis for Early Neurodegenerative Diseases Classification Through the Kinematic Theory of Rapid Human Movements. IEEE Access 2020, 8, 193966 -193980.
AMA StyleVincenzo Dentamaro, Donato Impedovo, Giuseppe Pirlo. Gait Analysis for Early Neurodegenerative Diseases Classification Through the Kinematic Theory of Rapid Human Movements. IEEE Access. 2020; 8 (99):193966-193980.
Chicago/Turabian StyleVincenzo Dentamaro; Donato Impedovo; Giuseppe Pirlo. 2020. "Gait Analysis for Early Neurodegenerative Diseases Classification Through the Kinematic Theory of Rapid Human Movements." IEEE Access 8, no. 99: 193966-193980.
In this paper, an automatic video diagnosis system for dementia classification is presented. Starting from video recordings of patients and control subjects, performing sit-to-stand test, the designed system is capable of extracting relevant patterns for binary discern patients with dementia from healthy subjects. The proposed system achieves an accuracy 0.808 by using the rigorous inter-patient separation scheme especially suited for medical purposes. This separation scheme provides the use of some people for training and others, different, people for testing. This work is an original and pioneering work on sit-to-stand video classification for neurodegenerative diseases, thus the novelty in this study is both on phases segmentation and experimental setup.
Vincenzo Dentamaro; Donato Impedovo; Giuseppe Pirlo. Sit-to-Stand Test for Neurodegenerative Diseases Video Classification. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 596 -609.
AMA StyleVincenzo Dentamaro, Donato Impedovo, Giuseppe Pirlo. Sit-to-Stand Test for Neurodegenerative Diseases Video Classification. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():596-609.
Chicago/Turabian StyleVincenzo Dentamaro; Donato Impedovo; Giuseppe Pirlo. 2020. "Sit-to-Stand Test for Neurodegenerative Diseases Video Classification." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 596-609.
The Internet of Things (IoT) paradigm applied to the agriculture field provides a huge amount of data allowing the employment of Artificial Intelligence for multiple tasks. In this work, solar radiation prediction is considered. To the aim, Multi-Layer Perceptron is adopted considering a complete real complex use case and real-time working conditions. More specifically the forecasting system is integrated considering three different time forecasting horizons and, given different sites, needs and data availability, multiple input features configurations have been considered. The described work allows companies to innovate and optimize their industrial business.
Donato Impedovo; Fabrizio Balducci; Giulio D’Amato; Michela Del Prete; Erminio Riezzo; Lucia Sarcinella; Mariagiorgia AgneseTandoi; Giuseppe Pirlo. An Application and Integration of Machine Learning Approach on a Real IoT Agricultural Scenario. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 474 -483.
AMA StyleDonato Impedovo, Fabrizio Balducci, Giulio D’Amato, Michela Del Prete, Erminio Riezzo, Lucia Sarcinella, Mariagiorgia AgneseTandoi, Giuseppe Pirlo. An Application and Integration of Machine Learning Approach on a Real IoT Agricultural Scenario. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():474-483.
Chicago/Turabian StyleDonato Impedovo; Fabrizio Balducci; Giulio D’Amato; Michela Del Prete; Erminio Riezzo; Lucia Sarcinella; Mariagiorgia AgneseTandoi; Giuseppe Pirlo. 2020. "An Application and Integration of Machine Learning Approach on a Real IoT Agricultural Scenario." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 474-483.
This work exploits Touch Dynamics to recognize affective states of a user while using a mobile device. To the aim, the acquired touch pattern is segmented in swipes, successively a wide set of handcrafted features is computed to characterize the swipe. The affective analysis is obtained through machine learning techniques. Data have been collected developing a specific App designed to acquire common unlock Android touch patterns. In this way the user interaction has been preserved as the more natural and neutral possible in real environments. Affective state labels have been obtained adopting a well-known psychological questionnaire. Three affective states have been considered: anxiety, stress and depression. Tests, performed on 115 users, reported an overall accuracy of 73.6% thus demonstrating the viability of the proposed approach.
Fabrizio Balducci; Donato Impedovo; Nicola Macchiarulo; Giuseppe Pirlo. Affective states recognition through touch dynamics. Multimedia Tools and Applications 2020, 79, 35909 -35926.
AMA StyleFabrizio Balducci, Donato Impedovo, Nicola Macchiarulo, Giuseppe Pirlo. Affective states recognition through touch dynamics. Multimedia Tools and Applications. 2020; 79 (47-48):35909-35926.
Chicago/Turabian StyleFabrizio Balducci; Donato Impedovo; Nicola Macchiarulo; Giuseppe Pirlo. 2020. "Affective states recognition through touch dynamics." Multimedia Tools and Applications 79, no. 47-48: 35909-35926.
This benchmarking study aims to examine and discuss the current state-of-the-art techniques for in-video violence detection, and also provide benchmarking results as a reference for the future accuracy baseline of violence detection systems. In this paper, the authors review 11 techniques for in-video violence detection. They re-implement five carefully chosen state-of-the-art techniques over three different and publicly available violence datasets, using several classifiers, all in the same conditions. The main contribution of this work is to compare feature-based violence detection techniques and modern deep-learning techniques, such as Inception V3.
Vito Nicola Convertini; Vincenzo Dentamaro; Donato Impedovo; Giuseppe Pirlo; Lucia Sarcinella. A Controlled Benchmark of Video Violence Detection Techniques. Information 2020, 11, 321 .
AMA StyleVito Nicola Convertini, Vincenzo Dentamaro, Donato Impedovo, Giuseppe Pirlo, Lucia Sarcinella. A Controlled Benchmark of Video Violence Detection Techniques. Information. 2020; 11 (6):321.
Chicago/Turabian StyleVito Nicola Convertini; Vincenzo Dentamaro; Donato Impedovo; Giuseppe Pirlo; Lucia Sarcinella. 2020. "A Controlled Benchmark of Video Violence Detection Techniques." Information 11, no. 6: 321.
Smart cities work under a more resource-efficient management and economy than ordinary cities. As such, advanced business models have emerged around smart cities, which have led to the creation of smart enterprises and organizations that depend on advanced technologies. In this Special Issue, 21 selected and peer-reviewed articles contributed in the wide spectrum of artificial intelligence applications to smart cities. Published works refer to the following areas of interest: vehicular traffic prediction; social big data analysis; smart city management; driving and routing; localization; and safety, health, and life quality.
Donato Impedovo; Giuseppe Pirlo. Artificial Intelligence Applications to Smart City and Smart Enterprise. Applied Sciences 2020, 10, 2944 .
AMA StyleDonato Impedovo, Giuseppe Pirlo. Artificial Intelligence Applications to Smart City and Smart Enterprise. Applied Sciences. 2020; 10 (8):2944.
Chicago/Turabian StyleDonato Impedovo; Giuseppe Pirlo. 2020. "Artificial Intelligence Applications to Smart City and Smart Enterprise." Applied Sciences 10, no. 8: 2944.
New Information and Communication Technologies have a large potential to improve general public awareness of the importance of Cultural Heritage (CH) and to provide tools that can make visits to historical sites more interesting and enjoyable. The Internet of Things (IoT) technology can further contribute to these goals, by allowing visitors to museum and CH sites to manipulate smart objects by receiving information that stimulates emotions, understanding and appropriation of the contents. In our research, interaction paradigms and innovative methods are developed to allow curators and guides of cultural sites (i.e., domain experts) to manage interactive IoT-based environments, in order to create Smart Interactive Experiences, which are usage situations created by synchronizing many available smart objects to specific situations that might better satisfy the needs of the visitors. This article illustrates a system that, by means of a tangible user interface, integrated by pattern recognition and computer vision techniques, supports CH experts in creating Smart Interactive Experiences by properly tailoring the behavior of the involved smart objects. An experimental evaluation of the used techniques has been performed and it is presented and discussed.
Fabrizio Balducci; Paolo Buono; Giuseppe Desolda; Donato Impedovo; Antonio Piccinno. Improving smart interactive experiences in cultural heritage through pattern recognition techniques. Pattern Recognition Letters 2019, 131, 142 -149.
AMA StyleFabrizio Balducci, Paolo Buono, Giuseppe Desolda, Donato Impedovo, Antonio Piccinno. Improving smart interactive experiences in cultural heritage through pattern recognition techniques. Pattern Recognition Letters. 2019; 131 ():142-149.
Chicago/Turabian StyleFabrizio Balducci; Paolo Buono; Giuseppe Desolda; Donato Impedovo; Antonio Piccinno. 2019. "Improving smart interactive experiences in cultural heritage through pattern recognition techniques." Pattern Recognition Letters 131, no. : 142-149.
Vehicular traffic flow prediction for a specific day of the week in a specific time span is valuable information. Local police can use this information to preventively control the traffic in more critical areas and improve the viability by decreasing, also, the number of accidents. In this paper, a novel generative deep learning architecture for time series analysis, inspired by the Google DeepMind’ Wavenet network, called TrafficWave, is proposed and applied to traffic prediction problem. The technique is compared with the most performing state-of-the-art approaches: stacked auto encoders, long–short term memory and gated recurrent unit. Results show that the proposed system performs a valuable MAPE error rate reduction when compared with other state of art techniques.
Donato Impedovo; Vincenzo Dentamaro; Giuseppe Pirlo; Lucia Sarcinella. TrafficWave: Generative Deep Learning Architecture for Vehicular Traffic Flow Prediction. Applied Sciences 2019, 9, 5504 .
AMA StyleDonato Impedovo, Vincenzo Dentamaro, Giuseppe Pirlo, Lucia Sarcinella. TrafficWave: Generative Deep Learning Architecture for Vehicular Traffic Flow Prediction. Applied Sciences. 2019; 9 (24):5504.
Chicago/Turabian StyleDonato Impedovo; Vincenzo Dentamaro; Giuseppe Pirlo; Lucia Sarcinella. 2019. "TrafficWave: Generative Deep Learning Architecture for Vehicular Traffic Flow Prediction." Applied Sciences 9, no. 24: 5504.
Automatic traffic flow classification is useful to reveal road congestions and accidents. Nowadays, roads and highways are equipped with a huge amount of surveillance cameras, which can be used for real-time vehicle identification, and thus providing traffic flow estimation. This research provides a comparative analysis of state-of-the-art object detectors, visual features, and classification models useful to implement traffic state estimations. More specifically, three different object detectors are compared to identify vehicles. Four machine learning techniques are successively employed to explore five visual features for classification aims. These classic machine learning approaches are compared with the deep learning techniques. This research demonstrates that, when methods and resources are properly implemented and tested, results are very encouraging for both methods, but the deep learning method is the most accurately performing one reaching an accuracy of 99.9% for binary traffic state classification and 98.6% for multiclass classification.
Donato Impedovo; Fabrizio Balducci; Vincenzo Dentamaro; Giuseppe Pirlo. Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison. Sensors 2019, 19, 5213 .
AMA StyleDonato Impedovo, Fabrizio Balducci, Vincenzo Dentamaro, Giuseppe Pirlo. Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison. Sensors. 2019; 19 (23):5213.
Chicago/Turabian StyleDonato Impedovo; Fabrizio Balducci; Vincenzo Dentamaro; Giuseppe Pirlo. 2019. "Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison." Sensors 19, no. 23: 5213.
In this paper, an automatic diagnosis system for neurodegenerative diseases is presented. Starting with an existing neurodegenerative diseases gait dataset, namely the NDDGD dataset, classification and regression algorithms have been trained, with the inter-patient dataset separation scheme (walking patterns used for training and testing, belong to different people), and integrated within a larger automatic diagnosis system which make use of videos in input or real-time streaming from cameras for predicting the neurodegenerative disease, if present, and its stage. The proposed system is capable of predicting among 3 neurodegenerative diseases, namely: amyotrophic lateral sclerosis disease (ALS), Parkinson’s disease (PD), Huntington’s disease (HUN) and differentiate among the severity (stage) level of the disease, if found. The system makes use of common cameras for the 2D pose estimation and features engineering. The system can be easily deployed in hospitals and houses in order to help physicians with the diagnosis. When used in conjunction with physicians, this system can be a valuable tool for neurodegenerative diseases prediction.
Vincenzo Dentamaro; Donato Impedovo; Giuseppe Pirlo. Real-Time Neurodegenerative Disease Video Classification with Severity Prediction. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 618 -628.
AMA StyleVincenzo Dentamaro, Donato Impedovo, Giuseppe Pirlo. Real-Time Neurodegenerative Disease Video Classification with Severity Prediction. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():618-628.
Chicago/Turabian StyleVincenzo Dentamaro; Donato Impedovo; Giuseppe Pirlo. 2019. "Real-Time Neurodegenerative Disease Video Classification with Severity Prediction." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 618-628.
Computer aided diagnosis systems can provide non-invasive, low-cost tools to support clinicians. These systems have the potential to assist the diagnosis and monitoring of neurodegenerative disorders, in particular Parkinson’s disease (PD). Handwriting plays a special role in the context of PD assessment. In this paper, the discriminating power of “dynamically enhanced” static images of handwriting is investigated. The enhanced images are synthetically generated by exploiting simultaneously the static and dynamic properties of handwriting. Specifically, we propose a static representation that embeds dynamic information based on: (i) drawing the points of the samples, instead of linking them, so as to retain temporal/velocity information; and (ii) adding pen-ups for the same purpose. To evaluate the effectiveness of the new handwriting representation, a fair comparison between this approach and state-of-the-art methods based on static and dynamic handwriting is conducted on the same dataset, i.e. PaHaW. The classification workflow employs transfer learning to extract meaningful features from multiple representations of the input data. An ensemble of different classifiers is used to achieve the final predictions. Dynamically enhanced static handwriting is able to outperform the results obtained by using static and dynamic handwriting separately.
Moises Diaz; Miguel Angel Ferrer; Donato Impedovo; Giuseppe Pirlo; Gennaro Vessio. Dynamically enhanced static handwriting representation for Parkinson’s disease detection. Pattern Recognition Letters 2019, 128, 204 -210.
AMA StyleMoises Diaz, Miguel Angel Ferrer, Donato Impedovo, Giuseppe Pirlo, Gennaro Vessio. Dynamically enhanced static handwriting representation for Parkinson’s disease detection. Pattern Recognition Letters. 2019; 128 ():204-210.
Chicago/Turabian StyleMoises Diaz; Miguel Angel Ferrer; Donato Impedovo; Giuseppe Pirlo; Gennaro Vessio. 2019. "Dynamically enhanced static handwriting representation for Parkinson’s disease detection." Pattern Recognition Letters 128, no. : 204-210.
Handwriting dynamics is relevant to discriminate people affected by neurodegenerative dementia from healthy subjects. This can be possible by administering simple and easy-to-perform handwriting/drawing tasks on digitizing tablets provided with electronic pens. Encouraging results have been recently obtained; however, the research community still lacks an acquisition protocol aimed at (i) collecting different traits useful for research purposes and (ii) supporting neurologists in their daily activities. This work proposes a handwriting-based protocol that integrates handwriting/drawing tasks and a digitized version of standard cognitive and functional tests already accepted, tested, and used by the neurological community. The protocol takes the form of a modular framework which facilitates the modification, deletion, and incorporation of new tasks in accordance with specific requirements. A preliminary evaluation of the protocol has been carried out to assess its usability. Successively, the protocol has been administered to more than 100 elderly MCI and match controlled subjects. The proposed protocol intends to provide a “cognitive model” for evaluating the relationship between cognitive functions and handwriting processes in healthy subjects as well as in cognitively impaired patients. The long-term goal of this research is the development of an easy-to-use and non-invasive methodology for detecting and monitoring neurodegenerative dementia during screening and follow-up.
Donato Impedovo; Giuseppe Pirlo; Gennaro Vessio; Maria Teresa Angelillo. A Handwriting-Based Protocol for Assessing Neurodegenerative Dementia. Cognitive Computation 2019, 11, 576 -586.
AMA StyleDonato Impedovo, Giuseppe Pirlo, Gennaro Vessio, Maria Teresa Angelillo. A Handwriting-Based Protocol for Assessing Neurodegenerative Dementia. Cognitive Computation. 2019; 11 (4):576-586.
Chicago/Turabian StyleDonato Impedovo; Giuseppe Pirlo; Gennaro Vessio; Maria Teresa Angelillo. 2019. "A Handwriting-Based Protocol for Assessing Neurodegenerative Dementia." Cognitive Computation 11, no. 4: 576-586.
This paper proposes a novel technique for an automatic detection of dementia based on the Attentional Matrices test (AMT) for selective attention assessment. The original test provides three matrices, of increasing difficulty, and the test taker is asked to mark target digits assigned. In our proposal, AMT was developed on a digitizing tablet, equipped with an electronic pen. Tablet technology enables the acquisition of additional measures to those that can be obtained by observing the execution of the traditional paper-based test. These measures reflect the dynamics of the handwriting process, particularly the pauses and hesitations while the pen is not in contact with the pad surface. Handwriting measures can then serve as input to machine learning algorithms to automatize the disease detection. In contrast to the traditional approach, dynamic handwriting analysis can provide a means to better evaluate the visual search of the patient, as well as her motor planning. To evaluate the effectiveness of the proposal, a classification study was carried out involving 29 healthy control subjects and 36 demented patients. We employed different machine learning algorithms and an ensemble scheme. We observed the first matrix to be the most discriminating; while, the ensemble of the best classification models over the three matrices provided the best classification performance (i.e., an AUC of 87.30% and a sensitivity of 86.11%). Our proposal has the potential to provide a cost-effective and easy-to-use diagnostic tool, which may also support a mass screening of the population.
Maria Teresa Angelillo; Fabrizio Balducci; Donato Impedovo; Giuseppe Pirlo; Gennaro Vessio. Attentional Pattern Classification for Automatic Dementia Detection. IEEE Access 2019, 7, 57706 -57716.
AMA StyleMaria Teresa Angelillo, Fabrizio Balducci, Donato Impedovo, Giuseppe Pirlo, Gennaro Vessio. Attentional Pattern Classification for Automatic Dementia Detection. IEEE Access. 2019; 7 (99):57706-57716.
Chicago/Turabian StyleMaria Teresa Angelillo; Fabrizio Balducci; Donato Impedovo; Giuseppe Pirlo; Gennaro Vessio. 2019. "Attentional Pattern Classification for Automatic Dementia Detection." IEEE Access 7, no. 99: 57706-57716.
Claudio De Stefano; Francesco Fontanella; Donato Impedovo; Giuseppe Pirlo; Alessandra Scotto di Freca. Handwriting analysis to support neurodegenerative diseases diagnosis: A review. Pattern Recognition Letters 2019, 121, 37 -45.
AMA StyleClaudio De Stefano, Francesco Fontanella, Donato Impedovo, Giuseppe Pirlo, Alessandra Scotto di Freca. Handwriting analysis to support neurodegenerative diseases diagnosis: A review. Pattern Recognition Letters. 2019; 121 ():37-45.
Chicago/Turabian StyleClaudio De Stefano; Francesco Fontanella; Donato Impedovo; Giuseppe Pirlo; Alessandra Scotto di Freca. 2019. "Handwriting analysis to support neurodegenerative diseases diagnosis: A review." Pattern Recognition Letters 121, no. : 37-45.
Artificial intelligence is changing the healthcare industry from many perspectives: diagnosis, treatment, and follow-up. A wide range of techniques has been proposed in the literature. In this special issue, 13 selected and peer-reviewed original research articles contribute to the application of artificial intelligence (AI) approaches in various real-world problems. Papers refer to the following main areas of interest: feature selection, high dimensionality, and statistical approaches; heart and cardiovascular diseases; expert systems and e-health platforms.
Donato Impedovo; Giuseppe Pirlo. eHealth and Artificial Intelligence. Information 2019, 10, 117 .
AMA StyleDonato Impedovo, Giuseppe Pirlo. eHealth and Artificial Intelligence. Information. 2019; 10 (3):117.
Chicago/Turabian StyleDonato Impedovo; Giuseppe Pirlo. 2019. "eHealth and Artificial Intelligence." Information 10, no. 3: 117.
Donato Impedovo. Velocity-Based Signal Features for the Assessment of Parkinsonian Handwriting. IEEE Signal Processing Letters 2019, 26, 632 -636.
AMA StyleDonato Impedovo. Velocity-Based Signal Features for the Assessment of Parkinsonian Handwriting. IEEE Signal Processing Letters. 2019; 26 (4):632-636.
Chicago/Turabian StyleDonato Impedovo. 2019. "Velocity-Based Signal Features for the Assessment of Parkinsonian Handwriting." IEEE Signal Processing Letters 26, no. 4: 632-636.
Handwritten signatures are biometric traits at the center of debate in the scientific community. Over the last 40 years, the interest in signature studies has grown steadily, having as its main reference the application of automatic signature verification, as previously published reviews in 1989, 2000, and 2008 bear witness. Ever since, and over the last 10 years, the application of handwritten signature technology has strongly evolved and much research has focused on the possibility of applying systems based on handwritten signature analysis and processing to a multitude of new fields. After several years of haphazard growth of this research area, it is time to assess its current developments for their applicability in order to draw a structured way forward. This perspective reports a systematic review of the last 10 years of the literature on handwritten signatures with respect to the new scenario, focusing on the most promising domains of research and trying to elicit possible future research directions in this subject.
Moises Diaz; Miguel A. Ferrer; Donato Impedovo; Muhammad Imran Malik; Giuseppe Pirlo; Réjean Plamondon. A Perspective Analysis of Handwritten Signature Technology. ACM Computing Surveys 2019, 51, 1 -39.
AMA StyleMoises Diaz, Miguel A. Ferrer, Donato Impedovo, Muhammad Imran Malik, Giuseppe Pirlo, Réjean Plamondon. A Perspective Analysis of Handwritten Signature Technology. ACM Computing Surveys. 2019; 51 (6):1-39.
Chicago/Turabian StyleMoises Diaz; Miguel A. Ferrer; Donato Impedovo; Muhammad Imran Malik; Giuseppe Pirlo; Réjean Plamondon. 2019. "A Perspective Analysis of Handwritten Signature Technology." ACM Computing Surveys 51, no. 6: 1-39.
Reduced training sets are major problems typically found on the task of offline signature verification. To increase the number of samples, the use of synthetic signatures can be taken into account. In this work, a new method for the generation of synthetic offline signatures by using dynamic and static (real) ones is presented. The synthesis is here faced under the perspective of supervised training: the learning model is trained to perform the task of online-to-offline signature conversion. The approach is based on a deep convolutional neural network. The main goal is to enlarge offline training dataset in order to improve performance of the offline signature verification systems. For this purpose, a machine-oriented evaluation on the BiosecurID signature dataset is carried out. The use of synthetic samples (in the training phase) generated with the proposed method on a state-of-the-art classification system exhibits performance similar to those obtained using real signatures; moreover, the combination of real and synthetic signatures in the training set is also able to show improvements of the equal error rate.
Victor K.S.L. Melo; Byron Leite Dantas Bezerra; Donato Impedovo; Giuseppe Pirlo; Antonio Lundgren. Deep learning approach to generate offline handwritten signatures based on online samples. IET Biometrics 2019, 8, 215 -220.
AMA StyleVictor K.S.L. Melo, Byron Leite Dantas Bezerra, Donato Impedovo, Giuseppe Pirlo, Antonio Lundgren. Deep learning approach to generate offline handwritten signatures based on online samples. IET Biometrics. 2019; 8 (3):215-220.
Chicago/Turabian StyleVictor K.S.L. Melo; Byron Leite Dantas Bezerra; Donato Impedovo; Giuseppe Pirlo; Antonio Lundgren. 2019. "Deep learning approach to generate offline handwritten signatures based on online samples." IET Biometrics 8, no. 3: 215-220.