[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122539-en":3,"doc-seo-122539-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122539,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",7,"Healthcare","Classification of Dementia Risk in the Elderly through Gait Analysis with Machine Learning Algorithms","Aging involves an irreversible, progressive decline of physiological functions, including brain aging that drives cognitive impairment and increases the risk of dementia. This cross-sectional observational study tested whether kinematic gait variables can identify and classify dementia risk using machine learning. The sample included 59 adults aged 60 ± 8 years, split into 26 institutionalized older adults and 33 non-institutionalized older adults in Bragança, Portugal. Gait was recorded during a 10-m walk with Kinovea, cognition measured by MMSE, and Python used to build models. Overall accuracy reached 74.6%, with AdaBoost highest at 83.5%; cross-validation yielded 72% overall, with SVM best individual performance at 80%. Gait analysis with machine learning supports safe, low-cost early identification for clinical and public health use.","Sport Sciences for Health (2025) 21:3387–3397  \n[https://doi.org/10.1007/s1](https://doi.org/10.1007/s1) 1332-025-01556-x  \nClassification of dementia risk in the elderly through gait analysis with machine learning algorithms  \nRaí Braz Costa1,2 · Samuel Gonçalves Almeida1,2 · Samuel Encarnação1,2,3 · André Schneider1,2 ·  \nJosé Eduardo Teixeira1,3,4,5,6,7 · Pedro Forte1,2,3,7 · António Miguel Monteiro1,2,3 · Tiago M. Barbosa1,2,3  \nReceived: 9 March 2025 / Revised: 28 July 2025 / Accepted: 18 September 2025 / Published online: 12 November 2025 © The Author(s) 2025  \nAbstract  \nThe 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 Bragança, 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™ 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.  \nKeywords Aging · Dementia · Gait analysis · Machine learning · Cognitive decline · Kinematic variables  \nIntroduction  \nAging is a biological process characterized by the progressive and irreversible decline of physiological functions, often associated with age-related diseases [1]. Among these changes, cognitive decline stands out, particularly affecting  \n* Samuel Encarnação  \nsamuel.goncalvesalmeida@estudiante.uam.es  \nAndré Schneider  \n[andrecschneider@gmail.com](andrecschneider@gmail.com)  \nAntónio Miguel Monteiro  \n[mmonteiro@ipb.pt](mmonteiro@ipb.pt)  \n1 Department of Physical Education, Sport and Human Movement, Universidad Autónoma de Madrid (UAM), Ciudad Universitaria de Cantoblanco, 28049 Madrid, Spain  \n2 Department of Sports Sciences, Instituto Politécnico de Bragança, 5300-253 Bragança, Portugal  \nlearning, memory, and executive functions [2] . These alterations are linked to structural and functional modifications in key brain regions such as the hippocampus and prefrontal cortex [3, 4] . Reduced neuroplasticity in the aging brain impairs the formation of new neural connections, increasing vulnerability to neurodegenerative conditions [5, 6] .  \n3 Research Centre for Active Living and Wellbeing (Livewell), Instituto Politécnico de Bragança, Bragança, Portugal  \n4 Department of Sports Sciences, Polytechnic of Guarda, Guarda, Portugal  \n5 Department of Sports Sciences, Polytechnic of Cávado and Ave, Guimarães, Portugal  \n6 SPRINT―Sport Physical Activity and Health Research and Innovation Center, Guarda, Portugal  \n7 CI-ISCE, ISCE Douro, Penafiel, Portugal  \nDementia, a clinical syndrome character","cbCaia3FuiJWGea0","https://ap.wps.com/l/cbCaia3FuiJWGea0","pdf",1317186,1,11,"English","en",105,"# Abstract\n# Introduction\n## Brain aging and cognitive decline\n## Dementia burden and need for early identification\n## Gait as a cognitive marker\n## Limitations of conventional assessments\n## Machine learning-based gait analysis rationale","[{\"question\":\"What was the aim of the study on older adults?\",\"answer\":\"To examine the feasibility of identifying and classifying dementia risk by analyzing kinematic variables from gait using machine learning algorithms.\"},{\"question\":\"How were gait and cognitive status assessed in the study?\",\"answer\":\"Gait data were collected during a 10-m walk using video recording and analyzed with Kinovea, while cognitive status was assessed using the Mini-Mental State Examination (MMSE).\"},{\"question\":\"Which machine learning methods performed best and what accuracy was achieved?\",\"answer\":\"AdaBoost achieved the best overall accuracy at 83.5%. With cross-validation, the Support Vector Machine (SVM) showed the highest individual performance at 80%, with correct classification of 80% of cases across different data subsets.\"}]","Classification of Dementia Risk in the Elderly through Gait Analysis with Machine Learning Algorithms | PDF",1785811174,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"classification-of-dementia-risk-in-the-elderly-through-gait-analysis-with-machine-learning-algorithms","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/classification-of-dementia-risk-in-the-elderly-through-gait-analysis-with-machine-learning-algorithms/122539/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What was the aim of the study on older adults?","Question",{"text":75,"@type":76},"To examine the feasibility of identifying and classifying dementia risk by analyzing kinematic variables from gait using machine learning algorithms.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were gait and cognitive status assessed in the study?",{"text":80,"@type":76},"Gait data were collected during a 10-m walk using video recording and analyzed with Kinovea, while cognitive status was assessed using the Mini-Mental State Examination (MMSE).",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning methods performed best and what accuracy was achieved?",{"text":84,"@type":76},"AdaBoost achieved the best overall accuracy at 83.5%. 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