{"id":{"repo_id":"radboud","oai_identifier":"oai:repository.ubn.ru.nl:2066/299449"},"canonical_url":"https://search.dev.ndltd.org/etd/radboud/oai:repository.ubn.ru.nl:2066/299449","repository":{"repo_id":"radboud","name":"Radboud University Nijmegen","base_url":"https://repository.ubn.ru.nl/oai/request"},"display":{"title":"Role of handwriting analysis through machine and deep learning to support the diagnosis of cognitive impairment","abstract":"Contains fulltext : 299449.pdf (Publisher’s version ) (Open Access)","abstract_html":"Contains fulltext : 299449.pdf (Publisher’s version ) (Open Access)","abstract_has_math":false,"creators":["Cilia, N.C."],"institution":"S.l. : s.n.","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Marchiori, E.","Fontanella, F."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023","date_published":"2023","updated_at":"2026-07-24T04:00:54Z","subjects":["Data Science"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://repository.ubn.ru.nl/handle/2066/299449","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Marchiori, E.","Fontanella, F."]},{"key":"dc:creator","label":"Author","values":["Cilia, N.C."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023"]},{"key":"dc:publisher","label":"Institution","values":["S.l. : s.n."]},{"key":"dc:type","label":"Dc Type","values":["Doctoral thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Data Science"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://repository.ubn.ru.nl//bitstream/handle/2066/299449/299449.pdf","https://repository.ubn.ru.nl/handle/2066/299449"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Contains fulltext : 299449.pdf (Publisher’s version ) (Open Access)","Alzheimer’s Disease(AD) is diagnosed by doctors using imaging, blood tests and lumbar punctures (spinal sampling) amongst others. Recently, researchers have shown that patients affected by AD have altered spatial organization and poor movement control. Therefore, the observation of motor activities should be used in the diagnosis of AD. Handwriting, which is the result of a complex network of cognitive, kinesthetic and perceptive motor skills, can be significantly compromised. In this framework, many studies have been published in the fields of medicine and psychology. However, these studies overlook the complex interactions that may occur between multiple features. In this thesis we collect the contributions of four journal papers and one conference paper. The aim of the thesis is to investigate how various machine learning and Deep Learning techniques can support the diagnosis of Alzheimer’s using handwriting as data source. After defining the experimental protocol and recruiting the subjects we presented a novel approach for the prediction of AD through the analysis of handwriting movements, training a set of classifiers, using a widely-used feature selection approach and some Convolutional Neural Networks (CNN).","Radboud University, 13 december 2023","Promotor : Marchiori, E. Co-promotor : Fontanella, F.","iii, 147 p."]},{"key":"dc:title","label":"Title","values":["Role of handwriting analysis through machine and deep learning to support the diagnosis of cognitive impairment"]}]}],"canonical_facts":{"dc:contributor":["Marchiori, E.","Fontanella, F."],"dc:creator":["Cilia, N.C."],"dc:date":["2023"],"dc:description":["Contains fulltext : 299449.pdf (Publisher’s version ) (Open Access)","Alzheimer’s Disease(AD) is diagnosed by doctors using imaging, blood tests and lumbar punctures (spinal sampling) amongst others. Recently, researchers have shown that patients affected by AD have altered spatial organization and poor movement control. Therefore, the observation of motor activities should be used in the diagnosis of AD. Handwriting, which is the result of a complex network of cognitive, kinesthetic and perceptive motor skills, can be significantly compromised. In this framework, many studies have been published in the fields of medicine and psychology. However, these studies overlook the complex interactions that may occur between multiple features. In this thesis we collect the contributions of four journal papers and one conference paper. The aim of the thesis is to investigate how various machine learning and Deep Learning techniques can support the diagnosis of Alzheimer’s using handwriting as data source. After defining the experimental protocol and recruiting the subjects we presented a novel approach for the prediction of AD through the analysis of handwriting movements, training a set of classifiers, using a widely-used feature selection approach and some Convolutional Neural Networks (CNN).","Radboud University, 13 december 2023","Promotor : Marchiori, E. Co-promotor : Fontanella, F.","iii, 147 p."],"dc:identifier":["https://repository.ubn.ru.nl//bitstream/handle/2066/299449/299449.pdf","https://repository.ubn.ru.nl/handle/2066/299449"],"dc:publisher":["S.l. : s.n."],"dc:subject":["Data Science"],"dc:title":["Role of handwriting analysis through machine and deep learning to support the diagnosis of cognitive impairment"],"dc:type":["Doctoral thesis"]},"updated_at":"2026-07-24T04:00:54Z"}