[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121254-en":3,"doc-seo-121254-105":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":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},121254,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Prediction of Perceived Exertion Ratings in National Level Soccer Players Using Wearable Sensor Data and Machine Learning Techniques","A study identifies relationships between external and internal training load parameters and subjective ratings of perceived exertion (RPE) for highly trained, national level soccer players. Using a dataset with 5402 training sessions and 732 match observations, the work integrates 174 heart rate, GPS, accelerometer, and Borg-scale RPE parameters from 26 professional male players. Nine machine learning models and one deep learning architecture are assessed via 5-fold cross-validation, with the deep model achieving the strongest RPE prediction accuracy.","©Journal of Sports Science and Medicine (2024) 23, 744-753  \n[http://www.jssm.org DOI:](http://www.jssm.org DOI:) [https://doi.org/10.52082/jssm.2024.744](https://doi.org/10.52082/jssm.2024.744)  \nResearch article  \nPrediction of Perceived Exertion Ratings in National Level Soccer Players Using Wearable Sensor Data and Machine Learning Techniques  \nRobert Leppich 1, Philipp Kunz 2, André Bauer 3, Samuel Kounev 1, Billy Sperlich 2 and Peter Düking 4 􀀍  \n1 Software Engineering Group, Department of Computer Science, University of Würzburg, Würzburg, Germany;  \n2 Integrative and Experimental Exercise Science and Training, Institute of Sport Science, University ofWürzburg, Würzburg, Germany; 3 Department of Computer Science, Illinois Institute of Technology, Chicago, United States of America; 4 Department of Sports Science and Movement Pedagogy, Technische Universität Braunschweig, Braunschweig, Germany  \nAbstract  \nThis study aimed to identify relationships between external and internal load parameters with subjective ratings of perceived exertion (RPE) . Consecutively, these relationships shall be used to evaluate different machine learning models and design a deep learning architecture to predict RPE in highly trained/national level soccer players. From a dataset comprising 5402 training sessions and 732 match observations, we gathered data on 174 distinct parameters, encompassing heart rate, GPS, accelerometer data and RPE (Borg’s 0-10 scale) of 26 professional male professional soccer players. Nine machine learning algorithms and one deep learning architecture was employed. Rigorous preprocessing protocols were employed to ensure dataset equilibrium and minimize bias. The efficacy and generalizability of these models were evaluated through a systematic 5-fold cross-validation approach. The deep learning model exhibited highest predictive power for RPE (Mean Absolute Error: 1.08 ± 0.07) . Treebased machine learning models demonstrated high-quality predictions (Mean Absolute Error: 1.15 ± 0.03) and a higher robustness against outliers. The strongest contribution to reducing the uncertainty of RPE with the tree-based machine learning models was maximal heart rate (determining 1.81% of RPE), followed by maximal acceleration (determining 1.48%) and total distance covered in speed zone 10-13 km/h (determining 1.44%) . A multitude of external and internal parameters rather than a single variable are relevant for RPE prediction in highly trained/national level soccer players, with maximum heart rate having the strongest influence on RPE. The ExtraTree Machine Learning model exhibits the lowest error rates for RPE predictions, demonstrates applicability to players not specifically considered in this investigation, and can be run on nearly any modern computer platform.  \nKey words: Machine learning, artificial intelligence, RPE, elite athletes, monitoring, training prescription.  \nIntroduction  \nWithin professional soccer, the quantification of both internal and external training loads holds a key role for tailoring training procedures to individual needs, with the ultimate goals of averting fatigue, mitigating the risk of illness and injury, and optimizing performance outcomes (Jones et al., 2017; Impellizzeri et al., 2023; Akenhead and Nassis, 2015) . In this context, the term \"internal load\" pertains to an individual's psychophysiological response to the external load (Schwellnus et al., 2016; Soligard et al.,  \n2016). Internal load parameters encompass factors like ratings of perceived exertion (RPE) and heart rate, while parameters such as the distance covered and accelerations are parameters of external load.  \nDespite the importance of monitoring internal and external load, there is no universally adopted monitoring approach in high-level soccer (Akenhead and Nassis, 2015) . More than 50 different external and internal load parameters are assessed in different high-level soccer clubs, and most soccer clubs employ external load pa","cbCaion96FRtKECF","https://ap.wps.com/l/cbCaion96FRtKECF","pdf",584317,1,10,"English","en",105,"# Introduction\n## Research Rationale and Background\n## Training Load Monitoring: Internal vs External\n## Machine Learning Motivation","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To identify relationships between external and internal load parameters and subjective RPE, then use these relationships to predict RPE using machine learning and deep learning models.\"},{\"question\":\"What data sources and scale are used to compute RPE?\",\"answer\":\"RPE is based on Borg’s 0–10 scale, derived alongside heart rate, GPS, and accelerometer data collected from training sessions and matches.\"},{\"question\":\"Which approach performed best for predicting RPE and what factors contributed most?\",\"answer\":\"The deep learning model showed the highest predictive power. With tree-based models, maximal heart rate contributed the most to reducing RPE uncertainty, followed by maximal acceleration and total distance in speed zone 10–13 km/h.\"}]","Prediction of Perceived Exertion Ratings in National Level Soccer Players Using Wearable Sensor Data and Machine Learning Techniques | PDF",1785734659,25,{"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},"prediction-of-perceived-exertion-ratings-in-national-level-soccer-players-using-wearable-sensor-data-and-machine-learning-techniques","",{"@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/prediction-of-perceived-exertion-ratings-in-national-level-soccer-players-using-wearable-sensor-data-and-machine-learning-techniques/121254/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of the study?","Question",{"text":75,"@type":76},"To identify relationships between external and internal load parameters and subjective RPE, then use these relationships to predict RPE using machine learning and deep learning models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data sources and scale are used to compute RPE?",{"text":80,"@type":76},"RPE is based on Borg’s 0–10 scale, derived alongside heart rate, GPS, and accelerometer data collected from training sessions and matches.",{"name":82,"@type":73,"acceptedAnswer":83},"Which approach performed best for predicting RPE and what factors contributed most?",{"text":84,"@type":76},"The deep learning model showed the highest predictive power. With tree-based models, maximal heart rate contributed the most to reducing RPE uncertainty, followed by maximal acceleration and total distance in speed zone 10–13 km/h.","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"]