Antonio Parziale

Antonio Parziale
Università degli Studi di Salerno | UNISA · Department of Information Engineering, Electrical Engineering and Applied Mathematics (DIEM)

PhD

About

47
Publications
20,779
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287
Citations
Citations since 2016
25 Research Items
249 Citations
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201620172018201920202021202201020304050
Introduction
Antonio Parziale currently works at the Department of Information Engineering, Electrical Engineering and Applied Mathematics (DIEM), Università degli Studi di Salerno. Antonio does research in Brain Modelling, Automatic Signature Verification System, Handwriting Analysis, Artificial Intelligence, Artificial Neural Network. He is involved in "Handwriting Analysis against Neuromuscular Disease" project.
Additional affiliations
January 2012 - May 2016
Università degli Studi di Salerno
Position
  • PhD Student

Publications

Publications (47)
Chapter
The analysis of handwriting and drawing has been adopted since the early studies to help diagnose neurodegenerative diseases, such as Alzheimer’s and Parkinson’s. Departing from the current state-of-the-art methods that approach the problem of discriminating between healthy subjects and patients by using two- or multi-class classifiers, we propose...
Article
Neurodegenerative diseases are caused by the progressive degeneration of nerve cells that affect motor skills and cognitive abilities with increasing severity. Unfortunately, there is no cure for this type of disease and their impact can only be slowed down with specific pharmacological and rehabilitative therapies. Early diagnosis, therefore, rema...
Preprint
Background: Reconstructing the trajectory from the static image of handwritten ink traces is useful in many practical applications envisaging handwriting analysis and recognition from off-line data, as it allows to use methods, algorithms and tools that deal with on-line data, achieving better results than those achieved on off-line data.Methods: I...
Chapter
Nowadays, different automatic systems for writer identification and verification are available. On-line writer identification through automatic analysis of handwriting acquired with a tablet has been widely studied. Furthermore, the recent development of Commercial Off-The-Shelf (COTS) wearables with integrated inertial measurement units (IMUs) rec...
Article
Full-text available
The order in which the trajectory is executed is a powerful source of information for recognizers. However, there is still no general approach for recovering the trajectory of complex and long handwriting from static images. Complex specimens can result in multiple pen-downs and in a high number of trajectory crossings yielding agglomerations of pi...
Article
In the last decades, early disease identification through non-invasive and automatic methodologies has gathered increasing interest from the scientific community. Among others, Parkinson's disease (PD) has received special attention in that it is a severe and progressive neuro-degenerative disease. As a consequence, early diagnosis would provide mo...
Article
Full-text available
Digital libraries offer access to a large number of handwritten historical documents. These documents are available as raw images and therefore their content is not searchable. A fully manual transcription is time-consuming and expensive while a fully automatic transcription is cheaper but not comparable in terms of accuracy. The performance of aut...
Article
Full-text available
The tradeoff between speed and accuracy of human movements has been exploited from many different perspectives, such as experimental psychology, workspace design, human–machine interface. This tradeoff is formalized by Fitts’ law, which states a linear relationship between the duration and the difficulty of the movement. The bigger is the required...
Chapter
Full-text available
We address the problem of designing a machine learning tool for the automatic diagnosis of Parkinson’s disease that is capable of providing an explanation of its behavior in terms that are easy to understand by clinicians. For this purpose, we consider as machine learning tool the decision tree, because it provides the decision criteria in terms of...
Chapter
Most handwriting recognition systems need a mechanism for handling classification errors. These errors are typically caused by the large shape variability of the handwriting produced by different writers and by the segmentation errors, which occur when the word recognition process is performed by extracting and classifying single characters. In thi...
Article
Building upon findings in computational model of handwriting learning and execution, we introduce the concept of stability to explain the difference between the actual movements performed during multiple execution of the subject’s signature, and conjecture that the most stable parts of the signature should play a paramount role in evaluating the si...
Conference Paper
Full-text available
Lognormality has proven to be an effective way for handwriting modeling. It assumes that handwriting is a time superimposition of a sequence of commands issued by the central nervous system, each command producing a stroke, i.e. a movement with a lognormal velocity profile. Motor control theories, however, suggest that handwriting movements result...
Conference Paper
Full-text available
This article propose a complete framework to recover the dynamic properties (i.e. velocity and pressure) of an on-line Western signature from an image-based signature. The framework is based on classical approaches to recover the writing order of the strokes and a novel process to recover the kinematic properties from thinned trajectories. In order...
Data
Masquerade is a software for the analysis and the evaluation of morphological and dynamic features of handwriting. The software is tailored for handwriting experts, both from forensic and HR sectors, and is able to automatically measure handwriting features and reduce the time needed to produce a report.
Conference Paper
Full-text available
Using an extracellular medium with high potassium/low magnesium concentration with the addition of 4-AP we induced epileptiform activity in combined hippocampus/entorhinal cortex slices of the rat brain [1]. In this in vitro model of temporal lobe epilepsy, we observed the repeating sequences of interictal discharge (IID) regimes and seizure-like e...
Conference Paper
Full-text available
Handwritten signature is a biometric trait used for verifying a person’s identity. Automatic signature verification systems typically require a lot of specimens in order to model the signing habit of a subject but, in a real scenario, few signature samples are available. To overcome this problem, methods for creating human-like duplicated signature...
Conference Paper
Full-text available
We present a method for writer identification that combines Forensic Handwriting Examination best practices with Pattern Recognition methodologies. The method is based upon a statistical characterization of the variability exhibited by a set of features that are meant to capture the distinctive aspects of document layout and handwriting. The featur...
Conference Paper
Full-text available
We present the motivations and the results of an investigation aimed at establishing to which extent bumps observed in the ink trace do not correspond necessarily to hesitation of the writing movements but can be observed in fluent handwriting as well. We will show by experiments that this latter case occurs very often and provide an explanation in...
Conference Paper
We propose a novel approach for helping content transcription of handwritten digital documents. The approach adopts a segmentation based keyword retrieval approach that follows query-by-string paradigm and exploits the user validation of the retrieved words to improve its performance during operation. Our approach starts with an initial training se...
Conference Paper
Full-text available
ICGenealogy: towards a common topology of neuronal ion channel function and genealogy in model and experiment Ion channels are fundamental constituents determining the function of single neurons and neuronal circuits. To understand their complex interactions, the field of computational modeling has proven essential: since its emergence, thousands...
Article
We suggest a model of signature verification based upon handwriting generation studies and derive from it the characterization of the signing habits of a subject. Such characterization is given in terms of the signature’s stability regions, which are obtained by exploiting shape and temporal information conveyed by the genuine signatures captured b...
Conference Paper
We propose a quantitative approach to both feature evaluation and comparison that combines Forensic Handwriting Examination best practices with Pattern Recognition methodologies. The former provide a set of features that are meant to capture the distinctive aspects of handwriting, the latter the computational tools for the quantitative evaluation o...
Conference Paper
Full-text available
The large majority of methods proposed in literature for handwriting recognition assume that words are produced drawing large parts of the ink without lifting the pen, other than horizontal bars and dots. This fundamental assumption, however, does not always hold: while some educational systems provide explicit training for producing continuous han...
Conference Paper
Full-text available
We present a model of the spinal cord in controlling one degree-of-freedom arm movements. The model includes both neural and musculoskeletal functions in an integrated framework. The model has been implemented by an artificial neural network coupled with a computational model of muscle publicly available. The experimental results show that the mode...
Conference Paper
We propose an algorithm based on a model of visual perception that is meant to reflect the human judgment about the similarity of handwritten samples. The algorithm builds upon the Fuzzy Feature Contrast model and proposes an implementation of such a model in the domain of handwriting. The algorithm has been validated on the RIMES dataset, by compa...
Data
Full-text available
Poster Presentation of a tool for supporting forensic document examiners.
Article
A novel definition of stability regions and a new method for detecting them from on-line signatures is introduced in this paper. Building upon handwriting generation and motor control studies, the stability regions is defined as the longest similar sequences of strokes between a pair of genuine signatures. The stability regions are then used to sel...
Conference Paper
Full-text available
We introduce a tool for quantitative evaluation of handwriting features largely adopted during forensic examination of questioned documents. The tool is based on a model of handwriting generation and execution according to which handwriting is composed of elementary movements, called strokes, whose order and timing of execution has been learned and...
Conference Paper
Full-text available
The form processing systems commercially available include a verification step during which a human operator verifies the output provided by the system to ensure 100% accuracy. In order to reduce the time and the cost of such a stage, the OCR engine incorporated into the system provides a reliability measure of the classification to be used for imp...
Conference Paper
We present an experimental validation of a model of handwriting style that builds upon a neuro-computational model of motor learning and execution. We hypothesize that handwriting style emerges from the concatenation of highly automated writing movements, called invariants, that have been learned by the subject in correspondence to the most frequen...
Conference Paper
We present a method for finding the stability regions within a set of genuine signatures and for selecting the most suitable one to be used for online signature verification. The definition of stability region builds upon motor learning and adaptation in handwriting generation, while their selection exploits both their ability to model signing habi...
Conference Paper
We discuss the dynamics of signatures in the light of recent findings in motor learning, according to which a signature is a highly automated motor task and, as such, it is stored in the brain as both a trajectory plan and a motor plan. We then conjecture that such a stored representation does not necessarily include the entire signature, but can b...
Conference Paper
We propose a new method for detecting the stability regions in on-line signatures. Building upon handwriting generation and motor control studies, the stability regions are defined as the longest common sequences of strokes between a pair of genuine signatures. The stability regions are then used to select the most stable signatures, as well as to...
Conference Paper
Full-text available
We present a study for modeling handwriting styles that derives from handwriting generation studies, according to which handwriting is a temporal sequence of elementary movements. Hence, handwriting style results from the way those movements are actually performed and sequentially executed to reach fluency. We conjecture that handwriting styles dep...
Conference Paper
Full-text available
The large majority of the methods proposed in literature for handwriting recognition assume that any word is produced without lifting the pen, other than horizontal bars and dots. This fundamental assumption, however, does not always hold: while some educational systems provide explicit training for producing continuous handwriting, minimizing the...
Conference Paper
Full-text available
We present a method for off-line reading of cursive handwriting, which derives from modelling handwriting as a complex movement. The method includes a step for recovering the writing order from static images of handwriting, a segmentation algorithm that decomposes the “unfolded” ink into strokes, an ink matching step to compare the ink of the unkno...

Questions

Questions (4)
Question
Dear all,
We know that each reflex involves a time delay between the stimulus and the reaction. This time delay is called reflex latency and It consists of three components:
  1. time of afferent conduction (Ta),
  2. central delay (Tc)
  3. time of efferent conduction (Te).
I want to model the reflex latency of the stretch and miotatic reflexes in human upper limb (In particular,  I'm interested in biceps, triceps and brachialis muscles).
In your opinion which are the best values for Ta , Tc and Te?
After reading different papers and books, my ideas is that good values could be:
Ta= 10 msec;
Te= 10 msec;
Tc= 0.5 msec if we hypothesize that the motoneuron has just one synapse.
So, the stretch reflex latency is equal to 20.5 msec and the golgi tendon reflex latency is equal to 21 msec
Question
Dear all, 
there are different studies supporting the hypothesis that the vertebrate motor system produces movements by combining a set of building blocks named motor primitives or motor synergies. 
One year ago, Levine and colleagues identified classes of interneurons in the mouse spinal cord that could support motor primitives in mammals (http://www.nature.com/neuro/journal/v17/n4/full/nn.3675.html).
I'm developing a computational model of the spinal cord and i would like to take into account these kind of networks but it seems that at the moment none know how to implement the motor primitives by a neurobiological point of view.
In particular, i want to investigate the role of this kind of spinal circuitry in the execution of reaching movements. D'avella and colleagues have shown (just for example here https://www.researchgate.net/publication/5818579_Combining_modules_for_movement) how a reaching movement can be decomposed in a linear combination of muscle synergies but it's a mathematical model.
Can you suggest me any papers that can help me to model a motor primitive circuitry? 
Thank you for your support,
Antonio
Question
In the last years I have studied how CNS and Spinal Cord interact for generating a reaching movement.
I'm writing on the current opinions about how CNS controls reaching movements. Because there are a lot of different positions about this topic i want to be sure that no one is omitted in my thesis.
So, in your opinion, which are the parameters encoded by the motor cortex in a motor command? Or, in other words, how CNS controls reaching movements?

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Projects

Projects (6)
Project
Movement is being investigated in biomechanics, neuroscience, psychology, and artificial intelligence. The progress that has been made in modeling and analyzing movement is contributing to both the understanding of human movement and the development of new applications. In particular, the availability of low cost and pervasive devices for recording movements (wearable devices, smartphones, tablets, cameras, etc.), together with machine learning methods for the quantitative and automatic analysis of movement, has put forward the development of systems for user authentication, medical diagnosis, and rehabilitation monitoring. A leading example is the use of artificial intelligence methods for the analysis of complex movements such as handwriting and gait, which is improving our knowledge of the mechanisms underlying human movement and, at the same time, enriching the fields of e-health and e-security with new applications. Another successful example is the design of control systems for prosthetic devices that exploit knowledge about movement execution and artificial intelligence methods. This Special Issue aims to highlight how movement analysis and new technologies are innovating the fields of health and biometric and contributing to human movement understanding. We invite researchers to contribute with original works and qualified reviews related to this Special Issue. For more information, please visit https://www.mdpi.com/journal/applsci/special_issues/Movement_Analysis_Health_Biometrics Deadline: October 31th, 2022