[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119025-en":3,"doc-seo-119025-105":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},119025,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",7,"Healthcare","Non-invasive Fitness Assessment in Horses - Integrating Wearables and Machine Learning","Human sports increasingly uses machine learning and sensors to analyze performance, yet sport horses lag because they cannot communicate fatigue or training difficulty in understandable ways. Traditional equine assessment relies on subjective verbal cues and facial expressions, or invasive blood sampling that adds stress, discomfort, and repeated interruptions to training. Inaccurate fitness parameters can undermine training plans, raising risks such as overtraining and injury. The thesis proposes wearable inertial sensors plus state-of-the-art machine learning for portable, field-ready, reliable fitness feedback, organized across nine chapters using realistic training and competition data to support performance improvement and injury prevention.","Non-invasive Fitness Assessment in Horses Integrating Wearables and Machine Learning  \nHamed Darbandi  \nNON-INVASIVE FITNESS ASSESSMENT IN HORSES  \nINTEGRATING WEARABLES AND MACHINE LEARNING  \nHamed Darbandi  \nNON-INVASIVE FITNESS ASSESSMENT IN HORSES  \nINTEGRATING WEARABLES AND MACHINE LEARNING  \nDISSERTATION  \nto obtain  \nthe degree of doctor at the University of Twente, on the authority of the rector magnificus,  \n[prof. dr. ir. A. Veldkamp](prof. dr. ir. A. Veldkamp),  \non account of the decision of the Doctorate Board to be publicly defended  \non Wednesday 26 June 2024 at 12.45 hours  \nby  \nHamed Darbandi  \nborn on the 24th of August, 1992  \nin Tehran, Iran  \nThis dissertation has been approved by:  \nPromotor  \nprof.dr. P.J.M. Havinga  \nCo-promotor  \n[dr.ir. B.J. van der Zwaag](dr.ir. B.J. van der Zwaag)  \nCover design: Hamed Darbandi  \nPrinted by: Drukkerij Ctrl-P  \nISBN (print): 978-90-365-6152-5  \nISBN (digital): 978-90-365-6153-2  \nURL: [https://doi.org/10.3990/1.9789036561532](https://doi.org/10.3990/1.9789036561532)  \n© 2024 Hamed Darbandi, The Netherlands. All rights reserved. No parts of this thesis may be reproduced, stored in a retrieval system or transmitted in any form or by any means without permission of the author. Alle rechten voorbehouden. Niets uit deze uitgave mag worden vermenigvuldigd, inenige vorm ofop enige wijze, zonder voorafgaande schriftelijke toestemming van de auteur.  \nGraduation Committee:  \nChair / secretary: Dean of the faculty EEMCS  \nPromotor: prof.dr. P.J.M. Havinga  \nUniversiteit Twente, EEMCS, Pervasive Systems  \nCo-promotor: [dr.ir. B.J. van der Zwaag](dr.ir. B.J. van der Zwaag)  \nUniversiteit Twente, EEMCS, Pervasive Systems  \nCommittee Members: [prof.dr.ir. P.H. Veltink](prof.dr.ir. P.H. Veltink)  \nUniversiteit Twente, EEMCS, Biomedical Signals and Systems  \n[prof.dr.ir. H.J. Hermens](prof.dr.ir. H.J. Hermens)  \nUniversiteit Twente, EEMCS, Biomedical Signals and Systems  \nprof. dr. P.R. van Weeren  \nUniversiteit Utrecht, Department of Clinical Sciences  \n[prof. dr.ir. M. Beigl](prof. dr.ir. M. Beigl)  \nKarlsruhe Institute of Technology (KIT)  \nIn memory of Paul Havinga  \nand all those who taught us to believe in ourselves  \nABSTRACT  \nThe field of human sports has seen remarkable technological advancements, incorporating machine learning and various sensors for performance analysis. However, the domain of sport horses lags in technological development. Unlike humans, horses lack the ability to provide easily understandable feedback, such as verbal expressions of fatigue or conveying the difficulty of a training session.  \nConventionally, veterinarians and researchers have devised methods to interpret equine well-being, including verbal encouragement, facial expressions, and blood sample analysis. The former two methods are subjective, relying on experienced individuals and laboratory environments for interpretation. The latter, while informative, is invasive, inducing stress and discomfort in horses during sample collection. It is also cumbersome, as it necessitates multiple interruptions to training sessions for sample collection. Furthermore, inaccurate or unreliable fitness parameter values can compromise the foundation of an effective training plan, potentially resulting in adverse outcomes such as overtraining and injury. Therefore, it is crucial to implement a method akin to those used in human sports, capable of providing feedback, and to choose a portable measuring device that can accurately and reliably assess fitness metrics. This device should be designed for field use, enabling assessments outside of a laboratory setting.  \nThis PhD thesis aims to revolutionize the training of sport horses by exploring the use of inertial sensors as wearable technology and the incorporation of state-of-the-art machine learning to enhance equine performance while preventing injuries. The study unfolds in nine chapters within two interconnected parts, with each contributing a crucial piec","cbCaimosyIV65mzj","https://ap.wps.com/l/cbCaimosyIV65mzj","pdf",57207400,1,178,"English","en",105,"# Abstract\n# Background and Motivation\n## Limits of current equine assessment\n## Need for portable, field-ready feedback\n# Thesis Aim and Approach\n## Wearable inertial sensors\n## Machine learning for performance and welfare\n# Study Structure\n## Two interconnected parts and nine chapters\n# Measurement System and Evaluation\n## Sensor placement on body segments\n## Training and competition scenarios\n# Key Outcomes and Impact\n## Motion data capture and insights for trainers and riders","[{\"question\":\"Why is a non-invasive fitness assessment approach needed for sport horses?\",\"answer\":\"Horses cannot give clear, verbal feedback about fatigue or training difficulty. Invasive methods like blood sampling cause stress and require interruptions, while subjective observations are limited by individual interpretation.\"},{\"question\":\"What role do wearable inertial sensors play in the thesis?\",\"answer\":\"Inertial sensors are selected to capture wide-ranging real-time motion data when placed on body segments such as the head, neck, shoulders, back, and legs.\"},{\"question\":\"How does machine learning improve equine performance and injury prevention?\",\"answer\":\"State-of-the-art machine learning analyzes collected motion data to provide reliable fitness parameter evaluation, helping trainers and riders adjust training and reduce risks like overtraining and injury.\"}]","Non-invasive Fitness Assessment in Horses - Integrating Wearables and Machine Learning | PDF",1785721977,449,{"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},"non-invasive-fitness-assessment-in-horses-integrating-wearables-and-machine-learning","",{"@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/non-invasive-fitness-assessment-in-horses-integrating-wearables-and-machine-learning/119025/",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},"Why is a non-invasive fitness assessment approach needed for sport horses?","Question",{"text":75,"@type":76},"Horses cannot give clear, verbal feedback about fatigue or training difficulty. Invasive methods like blood sampling cause stress and require interruptions, while subjective observations are limited by individual interpretation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role do wearable inertial sensors play in the thesis?",{"text":80,"@type":76},"Inertial sensors are selected to capture wide-ranging real-time motion data when placed on body segments such as the head, neck, shoulders, back, and legs.",{"name":82,"@type":73,"acceptedAnswer":83},"How does machine learning improve equine performance and injury prevention?",{"text":84,"@type":76},"State-of-the-art machine learning analyzes collected motion data to provide reliable fitness parameter evaluation, helping trainers and riders adjust training and reduce risks like overtraining and injury.","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},[93,97,101,105,110,115,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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":106,"slug":138},19,"General","general"]