Writing Generation Model for Health Care Neuromuscular System Investigation

Impedovo, Donato; Pirlo, Giuseppe; Mangini, F; Barbuzzi, Donato; Rollo, A. Di; Balestrucci, Alessandro; Impedovo, S.; Sarcinella, L.; O’Reilly, Christian; Plamondon, Réjean

doi:10.1007/978-3-319-09042-9_10

Cited by 21 publications

(18 citation statements)

References 4 publications

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“…In Reference [44], Impedovo et al investigated the relationship between the delta-lognormal and the sigma-lognormal models [49] and the early signs and symptoms of AD. The previously mentioned dataset ISUNIBA was collected and used to perform the analysis.…”

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confidence: 99%

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Vessio

2019

Applied Sciences

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Studying the effects of neurodegeneration on handwriting has emerged as an interdisciplinary research topic and has attracted considerable interest from psychologists to neuroscientists and from physicians to computer scientists. The complexity of handwriting, in fact, appears to be sensitive to age-related impairments in cognitive functioning; thus, analyzing handwriting in elderly people may facilitate the diagnosis and monitoring of these impairments. A large body of knowledge has been collected in the last thirty years thanks to the advent of new technologies which allow researchers to investigate not only the static characteristics of handwriting but also especially the dynamic aspects of the handwriting process. The present paper aims at providing an overview of the most relevant literature investigating the application of dynamic handwriting analysis in neurodegenerative disease assessment. The focus, in particular, is on Parkinon’s disease (PD) and Alzheimer’s disease (AD), as the two most widespread neurodegenerative disorders. More specifically, the studies taken into account are grouped in accordance with three main research questions: disease insight, disease monitoring, and disease diagnosis. The net result is that dynamic handwriting analysis is a powerful, noninvasive, and low-cost tool for real-time diagnosis and follow-up of PD and AD. In conclusion of the paper, open issues still demanding further research are highlighted.

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confidence: 99%

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Vessio

2019

Applied Sciences

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“…An evolution of this theory uses the sigma-lognormal model (ΣΛ). The effectiveness of this theory in predicting Parkinson Diseases through handwriting has been already demonstrated [1], [2], [4], [5]. The hypothesis at the very basis of this work is that transposing the Plamondon's kinematic theory of rapid human movements and its sigma-lognormal model (ΣΛ) from handwriting to gait analysis, it will still be possible to almost perfectly reconstruct and thus, model, the movement patterns of various body joints in terms of acceleration and velocity profiles.…”

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confidence: 87%

“…One of main examples of CAD tools for neurodegenerative disease assessment is handwriting [2], [3] analyzing both neuromuscular system parameters [4], and spatio-temporal models [5]. In particular, as reported in [1], [2], [4], [5] the sigma-lognormal (ΣΛ) model [8] of the kinematic theory of rapid human movements [9], [10] was employed, with successful results, in the early prediction of Parkinson Disease through handwriting [5]. In the review presented in [2] authors subdivide the state of art in neurodegenerative disease classification problem with handwriting into two main categories: computational and cognitive.…”

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confidence: 99%

Gait Analysis for Early Neurodegenerative Diseases Classification Through the Kinematic Theory of Rapid Human Movements

2020

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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. INDEX TERMS pose-estimation, computer vision, computer aided diagnosis, gait analysis, machine learning, early neurodegenerative diseases assessment, kinematic theory of rapid human movements, sigma-lognormal

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“…AI is growing rapidly, and its successful application in the eHealth domain is possibly due, in general, to the availability of massive datasets and computing resources. AI has found application in many medical branches: oncology [5,6], dermatology [7,8], radiology [9,10], neurology [11], neurodegenerative diseases [12,13], and many others. In general, a major topic of AI in medicine is related to the clinical decision support (CDS) to assist clinicians at the point of care [14,15].…”

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confidence: 99%

eHealth and Artificial Intelligence

Impedovo

Pirlo

2019

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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.

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Writing Generation Model for Health Care Neuromuscular System Investigation

Cited by 21 publications

References 4 publications

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Gait Analysis for Early Neurodegenerative Diseases Classification Through the Kinematic Theory of Rapid Human Movements

eHealth and Artificial Intelligence

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