[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118749-en":3,"doc-seo-118749-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},118749,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","MHfit - Mobile Health Data for Predicting Athletics Fitness Using Machine Learning","Smartphones and wearable devices enable collection of health data without manual entry, supporting real-time monitoring of vital signs. MHfit studies m-health data gathered from mobile sensors on ten volunteers performing multiple physical activities, then applies machine learning to analyze and predict health behavior relevant to athletic fitness decisions. Five algorithms are evaluated, with XGBoost delivering the strongest results, reaching 95.2% accuracy and high scores across sensitivity, specificity, and F1.","MHfit: Mobile Health Data for Predicting Athletics Fitness Using Machine Learning  \nJonayet Miah  \nDepartment of Computer Science University of South Dakota South Dakota, USA [jonayet.miah@coyotes.usd.edu](jonayet.miah@coyotes.usd.edu)  \nMd Ishtyaq Mahmud  \nCollege of Science & Engineering Central Michigan University Mount Pleasant, MI 48858, USA [mahmu4m@cmich.edu](mahmu4m@cmich.edu)  \n[asm.islam@coyotes.usd.ed](asm.islam@coyotes.usd.edu)[u](asm.islam@coyotes.usd.edu)  \nMuntasir Mamun  \nDepartment of Computer Science University of South Dakota South Dakota, USA[muntasir.mamun@coyotes.usd.edu](muntasir.mamun@coyotes.usd.edu)  \nAsm Mohaimenul Islam Department of Computer Science  \nUniversity of South Dakota South Dakota, USA  \nMd Minhazur Rahman  \nDepartment of Computer Science University of South Dakota South Dakota, USA [minhazur.rahman@coyotes.usd.edu](minhazur.rahman@coyotes.usd.edu)  \nSabbir Ahmad  \nDepartment of Mathematical Science University of South Dakota South Dakota, USA [sabbir.ahmad@coyotes.usd.edu](sabbir.ahmad@coyotes.usd.edu)  \nAbstract— Mobile phones and other electronic gadgets/devices have aided in collecting data without the need for data entry. This paper will specifically focus on Mobile health data(m-health). Mobile health data use mobile devices to gather clinical health data and track patients’ vitals in realtime. Our study is aimed to give decisions for small or big sports teams on whether one athlete good fit or not for a particular game with the compare several machine learning algorithms to predict human behavior and health using the data collected from mobile devices and sensors placed on patients. In this study, we have obtained the dataset from a similar study done on m-health. The dataset contains vital signs recordings of ten volunteers from different backgrounds. They had to perform several physical activities with a sensor placed on their bodies. Our study used 5 machine learning algorithms (XGBoost, Naïve Bayes, Decision Tree, Random Forest, and Logistic Regression) to analyze and predict human health behavior. XGBoost performed better compared to the other machine learning algorithms and achieved 95.2% in accuracy, 99.5% insensitivity, 99.5% in specificity, and 99.66% in F-1 score. Our research indicated a promising future in m-health being used to predict human behavior and further research and exploration need to be done for it to be available for commercial use specifically in the sports industry.  \nKeywords—Mobile Health data(m-health) -Artificial Intelligence-Machine learning.  \nI. INTRODUCTION  \nPersonal data is being regarded as a new economic asset. Our smartphone's personal data can be utilized for a variety of purposes, including identification, recommendation systems, predicting personalities by analyzing patterns in human behavior,  \nand logging human health data with sensors. Our research will focus on mobile health for human behavior analysis. Artificial intelligence (AI) gives us insight into data that can be used to revolutionize and transform different industries that will propel mankind to new heights. Artificial intelligence, specifically in the healthcare industry, can have a major impact on saving lives by providing solutions to pressing issues seen in healthcare. Mobile health is considered one of the main drivers that are seeking to bring this transformative change. According to M-health: Fundamentals and  \nApplications book, Mobile Health was first defined as ‘mobile computing, medical sensors, and communication technologies for healthcare [1] . The technological breakthrough connected with the release of the first smartphone had a significant impact on the evolution of m-Health. This also allowed for the creation of powerful embedded computational tools in smartphones and other technological devices(e.g., smart watches, wearable monitors, and sensors) to produce massive volumes of mobile health data. This innovative move ushered in the smartphonecentric m-Health ag","cbCaiuJgoUMP47BJ","https://ap.wps.com/l/cbCaiuJgoUMP47BJ","pdf",338655,1,6,"English","en",105,"# Introduction\n## Mobile health and AI in healthcare\n## Sports and fitness decision support\n# Methodology and algorithms\n## Dataset collection from m-health study\n## Machine learning models compared\n# Results and evaluation\n## XGBoost performance metrics\n# Discussion and future work\n## Commercial and sports-industry potential","[{\"question\":\"What problem does MHfit address?\",\"answer\":\"MHfit focuses on using mobile health (m-health) data to predict athletics fitness and help determine whether an athlete is a good fit for a specific game.\"},{\"question\":\"How is the dataset collected in this study?\",\"answer\":\"The study uses vital-sign recordings from ten volunteers. Participants perform several physical activities while sensors collect data from their bodies.\"},{\"question\":\"Which machine learning algorithm performed best?\",\"answer\":\"XGBoost outperformed the other models, achieving 95.2% accuracy and strong sensitivity, specificity, and F-1 score results.\"}]","MHfit - Mobile Health Data for Predicting Athletics Fitness Using Machine Learning | PDF",1785720038,15,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"mhfit-mobile-health-data-for-predicting-athletics-fitness-using-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@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/mhfit-mobile-health-data-for-predicting-athletics-fitness-using-machine-learning/118749/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does MHfit address?","Question",{"text":76,"@type":77},"MHfit focuses on using mobile health (m-health) data to predict athletics fitness and help determine whether an athlete is a good fit for a specific game.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the dataset collected in this study?",{"text":81,"@type":77},"The study uses vital-sign recordings from ten volunteers. Participants perform several physical activities while sensors collect data from their bodies.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning algorithm performed best?",{"text":85,"@type":77},"XGBoost outperformed the other models, achieving 95.2% accuracy and strong sensitivity, specificity, and F-1 score results.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},"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":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]