[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123883-en":3,"doc-seo-123883-105":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},123883,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",7,"Healthcare","Detection of mild cognitive impairment using various types of gait tests and machine learning","The research develops a practical, non-invasive approach for identifying mild cognitive impairment (MCI) by analyzing skeletal gait patterns captured with a Kinect v.2 depth camera. Participants walked on straight and oval paths while 25 body joints were recorded and processed through signal processing, descriptive statistics, and machine-learning modeling. The study shows that oval walking yields more discriminative gait features than straight walking. A Random Forest classifier performed best for MCI detection during oval walking, reaching 85.50% accuracy and 83.9% F-score.","TYPE Original Research PUBLISHED 11 July 2024  \nDOI 10. 3389/fneur.2024.1354092  \nOPEN ACCESS  \nEDITED BY  \nSebastian Moguilner,  \nHarvard Medical School, United States  \nREVIEWED BY  \nMario Martinez-Zarzuela, University of Valladolid, Spain Daniel Lu,  \nUniversity of California, Los Angeles, United States  \nCarlo Ricciardi,  \nUniversity of Naples Federico II, Italy  \n*CORRESPONDENCE  \nBehnaz Ghoraani  \n [bghoraani@fau.edu](bghoraani@fau.edu)  \nRECEIVED 11 December 2023  \nACCEPTED 27 June 2024  \nPUBLISHED 11 July 2024  \nCITATION  \nSeifallahi M, Galvin JE and Ghoraani B (2024) Detection of mild cognitive impairment using various types of gait tests and machine learning. Front. Neurol. 15:1354092 .  \ndoi: 10.3389/fneur.2024.1354092  \nCOPYRIGHT  \n© 2024 Seifallahi, Galvin and Ghoraani. This isan open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nDetection of mild cognitive impairment using various types of gait tests and machine learning  \nMahmoud Seifallahi1 , James E. Galvin2 and Behnaz Ghoraani1*  \n1 Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, United States, 2 Comprehensive Center for Brain Health, Department of Neurology, University of Miami, Boca Raton, FL, United States  \nIntroduction: Alzheimer’s disease and related disorders (ADRD) progressively impair cognitive function, prompting the need for early detection to mitigate its impact. Mild Cognitive Impairment (MCI) may signal an early cognitive decline due to ADRD. Thus, developing an accessible, non-invasive method for detecting MCI is vital for initiating early interventions to prevent severe cognitive deterioration.  \nMethods: This study explores the utility of analyzing gait patterns, a fundamental aspect of human motor behavior, on straight and oval paths for diagnosing MCI. Using a Kinect v.2 camera, we recorded the movements of 25 body joints from 25 individuals with MCI and 30 healthy older adults (HC) . Signal processing, descriptive statistical analysis, and machine learning techniques were employed to analyze the skeletal gait data in both walking conditions.  \nResults and discussion: The study demonstrated that both straight and oval walking patterns provide valuable insights for MCI detection, with a notable increase in identifiable gait features in the more complex oval walking test. The Random Forest model excelled among various algorithms, achieving an 85.50% accuracy and an 83 .9% F-score in detecting MCI during oval walking tests. This research introduces a cost-e􀀀ective, Kinect-based method that integrates gait analysis—a key behavioral pattern—with machine learning, o􀀀ering a practical tool for MCI screening in both clinical and home environments.  \nKEYWORDS  \nAlzheimer’s disease, mild cognitive impairment, human motor behavior, gait, depth camera, machine learning, signal processing  \n1 Introduction  \nAlzheimer’s disease (AD) and related dementias (ADRD) are progressive neurodegenerative diseases marked by neuronal damage and deterioration, leading to substantial cognitive impairments and a􀀓ecting cognitive functions such as memory, language, and problem-solving. In addition, many individuals with ADRD have gait and balance de􀀂cits (1–4) . As of 2023, approximately 6.7 million individuals in the United States aged 65 and above are estimated to live with AD, with projections indicating that this number is expected to swell to 13.8 million by 2060 (2) . Despite ongoing research, a cure for ADRD remains elusive, underscoring the critical importance of early detection for managing and slowing its progr","cbCaimQVkQ0BdND8","https://ap.wps.com/l/cbCaimQVkQ0BdND8","pdf",2390050,1,17,"English","en",105,"# Introduction\n## Methods\n## Results and discussion\n## Keywords","[{\"question\":\"How does the study detect mild cognitive impairment (MCI)?\",\"answer\":\"It detects MCI by analyzing gait patterns recorded as skeletal joint movements with a Kinect v.2 depth camera, then applying signal processing and machine-learning models.\"},{\"question\":\"What walking tests were used for the participants?\",\"answer\":\"Participants walked on straight and oval paths, and the models were evaluated on both walking conditions.\"},{\"question\":\"Which machine-learning model performed best, and what were the results?\",\"answer\":\"The Random Forest model performed best for MCI detection during oval walking, achieving 85.50% accuracy and an 83.9% F-score.\"}]","Detection of mild cognitive impairment using various types of gait tests and machine learning | 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does the study detect mild cognitive impairment (MCI)?","Question",{"text":75,"@type":76},"It detects MCI by analyzing gait patterns recorded as skeletal joint movements with a Kinect v.2 depth camera, then applying signal processing and machine-learning models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What walking tests were used for the participants?",{"text":80,"@type":76},"Participants walked on straight and oval paths, and the models were evaluated on both walking conditions.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine-learning model performed best, and what were the results?",{"text":84,"@type":76},"The Random Forest model performed best for MCI detection during oval walking, achieving 85.50% accuracy and an 83.9% 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