[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120883-en":3,"doc-seo-120883-105":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},120883,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Ataxic person prediction using feature optimized based on machine learning model - Ataxic person identification","Ataxic person identification through gait analysis relies on spatio-temporal signals from a Kinect sensor to support accurate distinction between ataxic and normal gait. Existing ML methods struggle to capture feature correlations across gait parameters and often produce high false positives under class-imbalanced data. The proposed approach uses an XGBoost-based classifier with enhanced feature optimization by modifying standard cross-validation, improving performance and robustness against imbalance.","Ataxic person prediction using feature optimized based on  \nmachine learning model  \nPavithra Durganivas Seetharama1, Shrishail Math2  \n1Department of Computer Science and Engineering, Canara Engineering College, Affiliated to Visvesvaraya Technological University,  \nMangalore, India  \n2Department of Computer Science and Engineering, Rajeev Institute of Technology, Affiliated to Visvesvaraya Technological  \nUniversity, Hassan, Karnataka, India  \nArticle history:  \nReceived Feb 4, 2023 Revised Nov 4, 2023 Accepted Nov 12, 2023  \nKeywords:  \nAtaxic person identification  \nBinary classification Class imbalance Deep learning Feature extraction  \nFeature selection Machine learning  \nCorresponding Author:  \nAtaxic gait monitoring and assessment of neurological disorders belong to important areas that are supported by digital signal processing methods and artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) techniques. This paper uses spatio-temporal data from Kinect sensor to optimize machine learning model to distinguish between ataxic and normal gait. Existing ML-based methodologies fails to establish feature correlation between different gait parameters; thus, exhibit very poor performance. Further, when data is imbalanced in nature the existing ML-based methodologies induces higher false positive. In addressing the research issues this paper introduces an extreme gradient boost (XGBoost) -based classifier and enhanced feature optimization (EFO) by modifying the standard cross validation (SCV) mechanism. Experiment outcome shows the proposed ataxic person identification model achieves very good result in comparison with existing ML-based and DL-based ataxic person identification methodologies.  \nThis is an open access article under the CC BY-SA license.  \nPavitra Durganivas Seetharama  \nResearch Scholar, Department of Computer Science and Engineering, Visvesvaraya Technological University  \nBelgavi 560018, Karnataka, India  \nEmail: [ds.pavithra88@gmail.com](ds.pavithra88@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nMotion disorders plays a key indicator in identifying many diseases as described in [1], [2] such as physical activity, rehabilitation, physical therapy, orthopedics, rheumatology, and neurology. As stated in [1], around 70% of neurological inpatients exhibit abnormal gait activity; thus, gait-based assessment model can be leveraged for early diagnosis [3] of neurological disorder as demonstrated in [4], [5] . The research work mainly focusses on identifying ataxic neurological disorder through gait analysis as demonstrated in [6] . Designing efficient gait-based mechanism that is skilled for automatic detection and nursing of neurological condition aid in enhancing treatment efficiency, diseases management and also further aid in reducing load of healthcare management environment as stated in [7], [8] . Tool and methodologies are needed for efficient detection and diagnosis of neurological disorders as shown in [9], [10] . The significant growth of wireless communication and sensor technology have led to usage of different sensors such as wearable devices [11], video, depth and thermal camera systems [12], microelectromechanical sensor units, and Kinect sensor [13] for monitoring different neurological disorder [14] . Recently, in [15] presented wearable gait sensor are efficient in studying the behavior of scale for the assessment and rating of ataxia (SARA) . Similarly, in [16] showed importance of studying gait characteristic of ataxic patient with multiple sclerosis [17], [18] .  \nThe machine learning technique have been adopted for detection and diagnosis of different neurological disorder such as flat fall prediction [19], [20], Parkinson [21], Friedreich's ataxia [22] . In [23] presented a deep leaning-based approach for cerebellar ataxic person identification [24] and compared with various machine learning model. All the existing deep learning-based approaches ","cbCaibIaWXw7l4Pe","https://ap.wps.com/l/cbCaibIaWXw7l4Pe","pdf",435588,1,10,"English","en",105,"# ABSTRACT\n# 1. INTRODUCTION\n## Gait as an indicator of neurological disorders\n## Sensors and data sources for gait monitoring\n## Machine learning and deep learning approaches\n## Motivation and proposed approach\n## Significance of FWO-XGB\n# Manuscript Organization\n## Section 2: existing atax","[{\"question\":\"为什么要进行ataxic人群的预测或识别？\",\"answer\":\"研究指出，异常步态能作为多种神经系统疾病的重要指标，约70%的神经科住院患者存在异常步态活动；因此基于步态的模型有助于早期诊断。\"},{\"question\":\"现有机器学习方法的主要不足是什么？\",\"answer\":\"文中指出，现有基于机器学习的方法难以建立不同步态参数之间的特征相关性，导致性能较差；同时在数据类别不平衡时会引入更高的假阳性。\"},{\"question\":\"本文提出的核心方法如何提升识别效果？\",\"answer\":\"方法采用基于XGBoost的分类器，并通过改进交叉验证机制进行增强特征优化；同时引入与误分类最小化相关的权重优化技术（FWO），以提升在不平衡数据下的准确性、特异度和敏感度。\"}]","Ataxic person prediction using feature optimized based on machine learning model - Ataxic person identification | PDF",1785732483,25,{"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},"ataxic-person-prediction-using-feature-optimized-based-on-machine-learning-model-ataxic-person-identification","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/ataxic-person-prediction-using-feature-optimized-based-on-machine-learning-model-ataxic-person-identification/120883/",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","2026-08-03",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},"为什么要进行ataxic人群的预测或识别？","Question",{"text":75,"@type":76},"研究指出，异常步态能作为多种神经系统疾病的重要指标，约70%的神经科住院患者存在异常步态活动；因此基于步态的模型有助于早期诊断。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"现有机器学习方法的主要不足是什么？",{"text":80,"@type":76},"文中指出，现有基于机器学习的方法难以建立不同步态参数之间的特征相关性，导致性能较差；同时在数据类别不平衡时会引入更高的假阳性。",{"name":82,"@type":73,"acceptedAnswer":83},"本文提出的核心方法如何提升识别效果？",{"text":84,"@type":76},"方法采用基于XGBoost的分类器，并通过改进交叉验证机制进行增强特征优化；同时引入与误分类最小化相关的权重优化技术（FWO），以提升在不平衡数据下的准确性、特异度和敏感度。","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]