Online Handwriting, Signature and Touch Dynamics: Tasks and Potential Applications in the Field of Security and Health

Faundez-Zanuy, Marcos; Mekyska, Jiří; Impedovo, Donato

doi:10.1007/s12559-021-09938-2

Cited by 17 publications

(8 citation statements)

References 109 publications

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“…As mentioned in the methodology, the Archimedean spiral is considered as a gold standard, especially in the assessment of graphomotor difficulties in PD patients [5,8,31], nevertheless, it has been utilised during the quantitative analysis of Huntington's disease, essential tremor, or brachial dystonia as well [13].…”

Section: Discussionmentioning

confidence: 99%

Prodromal Diagnosis of Lewy Body Diseases Based on the Assessment of Graphomotor and Handwriting Difficulties

Galáž

Mekyska

Mucha

et al. 2022

Lecture Notes in Computer Science

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To this date, studies focusing on the prodromal diagnosis of Lewy body diseases (LBDs) based on quantitative analysis of graphomotor and handwriting difficulties are missing. In this work, we enrolled 18 subjects diagnosed with possible or probable mild cognitive impairment with Lewy bodies (MCI-LB), 7 subjects having more than 50% probability of developing Parkinson's disease (PD), 21 subjects with both possible/probable MCI-LB and probability of PD > 50%, and 37 ageand gender-matched healthy controls (HC). Each participant performed three tasks: Archimedean spiral drawing (to quantify graphomotor difficulties), sentence writing task (to quantify handwriting difficulties), and pentagon copying test (to quantify cognitive decline). Next, we parameterized the acquired data by various temporal, kinematic, dynamic, spatial, and task-specific features. And finally, we trained classification models for each task separately as well as a model for their combination to estimate the predictive power of the features for the identification of LBDs. Using this approach we were able to identify prodromal LBDs with 74% accuracy and showed the promising potential of computerized objective and non-invasive diagnosis of LBDs based on the assessment of graphomotor and handwriting difficulties.

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Section: Discussionmentioning

confidence: 99%

Prodromal Diagnosis of Lewy Body Diseases Based on the Assessment of Graphomotor and Handwriting Difficulties

Galáž

Mekyska

Mucha

et al. 2022

Lecture Notes in Computer Science

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“…While the research into various aspects of automated systems for various tasks related to handwriting and signature analysis is significant, one relevant textbook [ 190 ] and104 articles [ [191] , [192] , [193] , [194] , [195] , [196] , [197] , [198] , [199] , [200] , [201] , [202] , [203] , [204] , [205] , [206] , [207] , [208] , [209] , [210] , [211] , [212] , [213] , [214] , [215] , [216] , [217] , [218] , [219] , [220] , [221] , [222] , [223] , [224] , [225] , [226] , [227] , [228] , [229] , [230] , [231] , [232] , [233] , [234] , [235] , [236] , [237] , [238] , [239] , [240] , [241] , [242] , [243] , [244] , [245] , [246] , [247] , [248] , [249] , [250] , [251] , [252] , ...…”

Section: Forensic Handwriting Examinationmentioning

confidence: 99%

Interpol questioned documents review 2019–2022

Deviterne-Lapeyre

Ibrahim²

2023

Forensic Science International: Synergy

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“…In recent years, digitizing traditional cognitive scales has become a hot spot in neuroscience [11]. Initially, researchers developed automatic scoring software for the ROCF test using arti cial intelligence (e.g., computer vision technology [12], Gaussian lter method [13], and deep machine learning algorithm [14; 15]), which can almost achieve 94% consistency with human scores.…”

Section: Introductionmentioning

confidence: 99%

A Tablet-based Quantitative Analysis: Diagnostic Value of Digital vs. Conventional Geriatric Complex Figure for Discriminating Patients with aMCI from Healthy Individuals

Zhang

et al. 2023

Preprint

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Background The population with dementia is expected to rise to 152 million in 2050 due to the aging population worldwide. Therefore, it is of great significance to identify and intervene in the early stage of dementia. The Rey-Osterreth complex figure (ROCF)test is a visuospatial test scale. Its scoring methods are numerous, time-consuming, and have poor consistency, which is unsuitable for wide application as required by the high number of people at risk. Therefore, there is an urgent need for a rapid, objective, and sensitive digital scoring method to accurately detect cognitive dysfunction in the early stage. Objective This study aims to clarify the organizational strategy of aMCI patients to draw complex figures through a multi-dimensional digital evaluation system. At the same time, a rapid, objective, and sensitive digital scoring method is established to replace traditional scoring. Methods the data of 64 subjects (38 aMCI patients and 26 NC individuals) were analyzed in this study. All subjects completed the tablet's Geriatric Complex Figure (GCF) test, including copying, 3-min recall, and 20-min delayed recall, and also underwent a standardized neuropsychological test battery and classic ROCF test. Digital GCF(dGCF) variables and conventional GCF(cGCF) scores were input into the forward stepwise logistic regression model to construct classification models. Finally, ROC curves were made to visualize the difference in the diagnostic value of dGCF variables vs. cGCF scores in categorizing the diagnostic groups. Results In 20 min delayed recall, the time in air and pause time of aMCI patients were longer than NC individuals. And patients with aMCI had more short strokes and poorer ability of detail integration (all p < 0.05). The diagnostic sensitivity of dGCF variables for aMCI patients was 89.47%, slightly higher than cGCF scores (sensitivity: 84.21%). The diagnostic accuracy of both was comparable (dGCF: 70.3%; cGCF: 73.4%). Moreover, the combination of dGCF variables and cGCF scores could significantly improve the diagnostic accuracy and specificity (accuracy:78.1%, specificity: 84.62%). At the same time, we construct the regression equations of the two models. Conclusions Our study shows that dGCF equipment can quantitatively evaluate drawing performance, and its performance is comparable to the time-consuming cGCF score. The regression equation of the model we constructed can well identify patients with aMCI in clinical application. We believe that this new approach has the potential to become a digital biomarker for MCI patients.

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Online Handwriting, Signature and Touch Dynamics: Tasks and Potential Applications in the Field of Security and Health

Cited by 17 publications

References 109 publications

Prodromal Diagnosis of Lewy Body Diseases Based on the Assessment of Graphomotor and Handwriting Difficulties

Prodromal Diagnosis of Lewy Body Diseases Based on the Assessment of Graphomotor and Handwriting Difficulties

Interpol questioned documents review 2019–2022

A Tablet-based Quantitative Analysis: Diagnostic Value of Digital vs. Conventional Geriatric Complex Figure for Discriminating Patients with aMCI from Healthy Individuals

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