Spiral drawing analysis with a smart ink pen to identify Parkinson's disease fine motor deficits
IntroductionSince the uptake of digitizers, quantitative spiral drawing assessment allowed gaining insight into motor impairments related to Parkinson's disease. However, the reduced naturalness of the gesture and the poor user-friendliness of the data acquisition hamper the adoption of such te...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2023.1093690/full |
| _version_ | 1811167254486712320 |
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| author | Simone Toffoli Francesca Lunardini Monica Parati Monica Parati Matteo Gallotta Beatrice De Maria Luca Longoni Maria Elisabetta Dell'Anna Simona Ferrante |
| author_facet | Simone Toffoli Francesca Lunardini Monica Parati Monica Parati Matteo Gallotta Beatrice De Maria Luca Longoni Maria Elisabetta Dell'Anna Simona Ferrante |
| author_sort | Simone Toffoli |
| collection | DOAJ |
| description | IntroductionSince the uptake of digitizers, quantitative spiral drawing assessment allowed gaining insight into motor impairments related to Parkinson's disease. However, the reduced naturalness of the gesture and the poor user-friendliness of the data acquisition hamper the adoption of such technologies in the clinical practice. To overcome such limitations, we present a novel smart ink pen for spiral drawing assessment, intending to better characterize Parkinson's disease motor symptoms. The device, used on paper as a normal pen, is enriched with motion and force sensors.MethodsForty-five indicators were computed from spirals acquired from 29 Parkinsonian patients and 29 age-matched controls. We investigated between-group differences and correlations with clinical scores. We applied machine learning classification models to test the indicators ability to discriminate between groups, with a focus on model interpretability.ResultsCompared to control, patients' drawings were characterized by reduced fluency and lower but more variable applied force, while tremor occurrence was reflected in kinematic spectral peaks selectively concentrated in the 4–7 Hz band. The indicators revealed aspects of the disease not captured by simple trace inspection, nor by the clinical scales, which, indeed, correlate moderately. The classification achieved 94.38% accuracy, with indicators related to fluency and power distribution emerging as the most important.ConclusionIndicators were able to significantly identify Parkinson's disease motor symptoms. Our findings support the introduction of the smart ink pen as a time-efficient tool to juxtapose the clinical assessment with quantitative information, without changing the way the classical examination is performed. |
| first_indexed | 2024-04-10T16:06:12Z |
| format | Article |
| id | doaj.art-a14e617a648c42479d56c7a0cc086f80 |
| institution | Directory Open Access Journal |
| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-04-10T16:06:12Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj.art-a14e617a648c42479d56c7a0cc086f802023-02-10T04:56:54ZengFrontiers Media S.A.Frontiers in Neurology1664-22952023-02-011410.3389/fneur.2023.10936901093690Spiral drawing analysis with a smart ink pen to identify Parkinson's disease fine motor deficitsSimone Toffoli0Francesca Lunardini1Monica Parati2Monica Parati3Matteo Gallotta4Beatrice De Maria5Luca Longoni6Maria Elisabetta Dell'Anna7Simona Ferrante8Nearlab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, ItalyChild Neuropsychiatry Unit, Department of Child Neurology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, ItalyNearlab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, ItalyIstituti Clinici Scientifici Maugeri IRCCS, Milano, ItalyIstituti Clinici Scientifici Maugeri IRCCS, Milano, ItalyIstituti Clinici Scientifici Maugeri IRCCS, Milano, ItalyIstituti Clinici Scientifici Maugeri IRCCS, Lissone, ItalyIstituti Clinici Scientifici Maugeri IRCCS, Milano, ItalyNearlab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, ItalyIntroductionSince the uptake of digitizers, quantitative spiral drawing assessment allowed gaining insight into motor impairments related to Parkinson's disease. However, the reduced naturalness of the gesture and the poor user-friendliness of the data acquisition hamper the adoption of such technologies in the clinical practice. To overcome such limitations, we present a novel smart ink pen for spiral drawing assessment, intending to better characterize Parkinson's disease motor symptoms. The device, used on paper as a normal pen, is enriched with motion and force sensors.MethodsForty-five indicators were computed from spirals acquired from 29 Parkinsonian patients and 29 age-matched controls. We investigated between-group differences and correlations with clinical scores. We applied machine learning classification models to test the indicators ability to discriminate between groups, with a focus on model interpretability.ResultsCompared to control, patients' drawings were characterized by reduced fluency and lower but more variable applied force, while tremor occurrence was reflected in kinematic spectral peaks selectively concentrated in the 4–7 Hz band. The indicators revealed aspects of the disease not captured by simple trace inspection, nor by the clinical scales, which, indeed, correlate moderately. The classification achieved 94.38% accuracy, with indicators related to fluency and power distribution emerging as the most important.ConclusionIndicators were able to significantly identify Parkinson's disease motor symptoms. Our findings support the introduction of the smart ink pen as a time-efficient tool to juxtapose the clinical assessment with quantitative information, without changing the way the classical examination is performed.https://www.frontiersin.org/articles/10.3389/fneur.2023.1093690/fullsmart ink penspiral analysisParkinson's diseasemovement disorderseHealth |
| spellingShingle | Simone Toffoli Francesca Lunardini Monica Parati Monica Parati Matteo Gallotta Beatrice De Maria Luca Longoni Maria Elisabetta Dell'Anna Simona Ferrante Spiral drawing analysis with a smart ink pen to identify Parkinson's disease fine motor deficits Frontiers in Neurology smart ink pen spiral analysis Parkinson's disease movement disorders eHealth |
| title | Spiral drawing analysis with a smart ink pen to identify Parkinson's disease fine motor deficits |
| title_full | Spiral drawing analysis with a smart ink pen to identify Parkinson's disease fine motor deficits |
| title_fullStr | Spiral drawing analysis with a smart ink pen to identify Parkinson's disease fine motor deficits |
| title_full_unstemmed | Spiral drawing analysis with a smart ink pen to identify Parkinson's disease fine motor deficits |
| title_short | Spiral drawing analysis with a smart ink pen to identify Parkinson's disease fine motor deficits |
| title_sort | spiral drawing analysis with a smart ink pen to identify parkinson s disease fine motor deficits |
| topic | smart ink pen spiral analysis Parkinson's disease movement disorders eHealth |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2023.1093690/full |
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