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Classification of dementia risk in the elderly through gait analysis with machine learning algorithms

2025Journal article Open access
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datacite.subject.fosCiências Médicas::Ciências da Saúde
datacite.subject.fosCiências Médicas::Outras Ciências Médicas
datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg10:Reduzir as Desigualdades
dc.contributor.authorCosta, Raí Braz
dc.contributor.authorAlmeida, Samuel Gonçalves
dc.contributor.authorEncarnação, Samuel
dc.contributor.authorSchneider, André
dc.contributor.authorBarbosa, Tiago M.
dc.contributor.authorTeixeira, José Eduardo
dc.contributor.authorForte, Pedro
dc.contributor.authorMonteiro, António M.
dc.date.accessioned2026-01-06T11:06:27Z
dc.date.available2026-01-06T11:06:27Z
dc.date.issued2025
dc.description.abstractThe irreversible and progressive decline of physiological functions is known as aging. Among these changes is brain aging, which leads to cognitive decline and the onset of dementia. This directly affects memory, learning, and motor skills, reducing gait efficiency. This study aimed to investigate the feasibility of identifying and classifying the risk of dementia based on the analysis of kinematic variables related to gait in older adults using machine learning algorithms. This cross-sectional observational study examined a sample of 59 individuals aged 60 +/- 8 years, divided into two groups: 26 institutionalized older adults (GI) and 33 non-institutionalized older adults (GNI), all residing in Bragan & ccedil;a, Portugal. Gait data were collected during a 10-m walk, recorded on video, and analyzed using Kinovea software. Cognitive status was assessed using the Mini-Mental State Examination (MMSE). Python (TM) was used for statistical analysis and to develop machine learning models to classify dementia risk based on gait variables. The results showed that the algorithmic models achieved an overall accuracy of 74.6%, with the AdaBoost algorithm performing best at 83.5%. Cross-validation revealed an overall accuracy of 72%, with the Support Vector Machine (SVM) classifier achieving the highest individual performance at 80%, correctly classifying 80% of cases across different data subsets. In conclusion, gait analysis combined with machine learning algorithms demonstrated a strong relationship between gait variables and dementia, proving to be a safe and efficient technique for dementia classification. This approach offers a low-cost and accessible early identification and intervention method, with potential applications in clinical and public health settings.eng
dc.description.sponsorshipOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
dc.identifier.citationCosta, Raí Braz; Almeida, Samuel Gonçalves; Encarnação, Samuel; Schneider, Andre; Teixeira, Jose Eduardo; Forte, Pedro; Monteiro, Antonio Miguel; Barbosa, Tiago M. (2025). Classification of dementia risk in the elderly through gait analysis with machine learning algorithms. Sport Sciences for Health. ISSN 1825-1234. 21:4, p. 3387-3397.
dc.identifier.doi10.1007/s11332-025-01556-x
dc.identifier.eissn1825-1234
dc.identifier.issn1824-7490
dc.identifier.urihttp://hdl.handle.net/10198/35324
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Science and Business Media LLC
dc.relation.ispartofSport Sciences for Health
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectAging
dc.subjectDementia
dc.subjectGait analysis
dc.subjectMachine learning
dc.subjectCognitive decline
dc.subjectKinematic variables
dc.titleClassification of dementia risk in the elderly through gait analysis with machine learning algorithmseng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage3397
oaire.citation.issue4
oaire.citation.startPage3387
oaire.citation.titleSport Sciences for Health
oaire.citation.volume21
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameEncarnação
person.familyNameSchneider
person.familyNameBarbosa
person.familyNameTeixeira
person.familyNameForte
person.familyNameMonteiro
person.givenNameSamuel
person.givenNameAndré
person.givenNameTiago M.
person.givenNameJosé Eduardo
person.givenNamePedro
person.givenNameAntónio M.
person.identifier.ciencia-id9416-E2F5-E660
person.identifier.ciencia-id6618-FA6F-C0E4
person.identifier.ciencia-id8B11-BDC4-F6FF
person.identifier.ciencia-idD11C-9591-7A8A
person.identifier.ciencia-id351B-B16B-79C7
person.identifier.ciencia-idC41C-6CCD-A1F0
person.identifier.orcid0000-0003-2965-2777
person.identifier.orcid0009-0003-4856-9706
person.identifier.orcid0000-0001-7071-2116
person.identifier.orcid0000-0003-4612-3623
person.identifier.orcid0000-0003-0184-6780
person.identifier.orcid0000-0003-4467-1722
person.identifier.scopus-author-id10044856400
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