Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/116097
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dc.contributor.authorToffoli, Simoneen_US
dc.contributor.authorLunardini, Francescaen_US
dc.contributor.authorParati, Monicaen_US
dc.contributor.authorGallotta, Matteoen_US
dc.contributor.authorMuletti, Manuelen_US
dc.contributor.authorBelloni, Chiaraen_US
dc.contributor.authorDell’Anna, Maria Elisabettaen_US
dc.contributor.authorFerrante, Simonaen_US
dc.date.accessioned2022-07-05T08:56:53Z-
dc.date.available2022-07-05T08:56:53Z-
dc.date.issued2022en_US
dc.identifier.urihttp://hdl.handle.net/10553/116097-
dc.description.abstractSystems for monitoring of Parkinson’s disease (PD) patients, able to complement clinical assessment, are needed. These solutions should be objective, based on technology that captures physical characteristics of the pathology, and capable of providing frequent measures conducted both on-site and remotely. Since one of the most typical clinical hallmarks of PD is handwriting deterioration, we devised an innovative smart ink pen for quantitative and reliable handwriting monitoring, without altering the natural writing conditions. 30 PD patients and 30 age-matched controls performed two unconstrained writing tasks (free text and grocery list) with the smart ink pen. A series of 47 writing and tremor indicators were computed and used to classify patients from age-matched controls. Catboost and Logistic Regression classifiers were used, and the SHAP model explanation technique was applied to explore the contribution of the features in the classification. Very good performances were obtained through the Catboost classifier when combining features extracted from both tasks (Accuracy: 93%, Precision: 96%, Recall: 90%; F1: 93%; AUC: 98.9%). We achieved a classification performance in line with previous work, with two main advantages: writing data acquisition through an ink pen used on common paper, and proposition of an unconstrained protocol mimicking daily-life writing.en_US
dc.languageengen_US
dc.sourceThe 20th Conference of the International Graphonomics Society (IGS2021). Conference proceedings for short papers not published in the LNCS – Springeren_US
dc.subject570110 Patología y corrección del lenguajeen_US
dc.subject320711 Neuropatologíaen_US
dc.titleClassification of Patients with Parkinson’s Disease Using Free Handwriting Features Collected through a Smart Ink Penen_US
dc.typeinfo:eu-repo/semantics/conferenceobjecten_US
dc.typeConferenceObjecten_US
dc.relation.conference20th Conference of the International Graphonomics Society (IGS 2021)en_US
dc.investigacionCiencias de la Saluden_US
dc.type2Actas de congresosen_US
dc.description.numberofpages4en_US
dc.utils.revisionen_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-BASen_US
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.event.eventsstartdate07-06-2022-
crisitem.event.eventsenddate09-06-2022-
Appears in Collections:Actas de congresos
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