[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126364-en":3,"doc-seo-126364-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126364,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","A machine learning approach for saddle height classification in cycling - research findings","A data-driven machine learning pipeline predicts saddle height categories for cyclists using input signals related to cycling dynamics. The study evaluates feature representations derived from joint angles, motion-derived variables, and multiple sensor-derived measures to learn a classification model. Model performance is reported across different conditions, with results indicating strong agreement between predicted and reference classes and high classification accuracy under the tested setup. The work frames saddle-height recognition as a supervised learning task to support training and equipment-related decision making.","TYPE O􀀃􀀄􀀐􀀄􀀈􀀇􀀘 R􀀌􀀅􀀌􀀇􀀃 􀀂 PU􀀗􀀞ISHED 17 S􀀌􀀗􀀆􀀌􀀜􀀏􀀌􀀃 2025 DOI 10.3389/􀀛􀀅􀀗􀀖􀀃 .2025.1607212  \n􀀁􀀂􀀃􀀄􀀁􀀂 􀀆􀀇  \n􀀁􀀂􀀃􀀄􀀅􀀆􀀄􀀇􀀈 􀀊􀀇􀀋􀀃􀀌􀀃􀀍􀀎􀀃􀀋􀀏􀀄􀀈􀀐􀀌􀀃􀀑  \n􀀒􀀈􀀄􀀓􀀌􀀃􀀅􀀄􀀆􀀔 􀀕􀀖􀀅􀀗􀀄􀀆􀀇􀀘 􀀙􀀃􀀇􀀈􀀚􀀛􀀋􀀃􀀆􀀑 􀀎􀀌􀀃􀀜􀀇􀀈􀀔  \n􀀈􀀁􀀉􀀃􀀁􀀊􀀁􀀂 􀀆􀀇  \n􀀝􀀇􀀖􀀘􀀖 􀀎􀀇􀀛􀀛􀀋􀀃􀀄􀀈􀀄􀀑􀀒􀀈􀀄􀀓􀀌􀀃􀀅􀀄􀀆􀀔 􀀖􀀛 􀀞􀀃􀀌􀀅 􀀄􀀇􀀑 I􀀆􀀇􀀘􀀔 D􀀇􀀆􀀇􀀖 X􀀋􀀑 N􀀄􀀈􀀐􀀏􀀖 􀀒􀀈􀀄􀀓􀀌􀀃􀀅􀀄􀀆􀀔􀀑 􀀁􀀂􀀄􀀈􀀇  \n*􀀋􀀌􀀈􀀈􀀁􀀍􀀎􀀌􀀏􀀂􀀁􀀏􀀋􀀁  \n􀀊􀀄􀀈􀀐 Z􀀂􀀇􀀈􀀐  \n 􀀜􀀄􀀈􀀐 .z􀀂􀀇􀀈􀀐@􀀗􀀖􀀘􀀔􀀋 .􀀌d􀀋 . 􀀂􀀚  \n􀀈􀀁􀀋􀀁􀀃􀀉􀀁􀀂 07 A􀀗􀀃􀀄􀀘 2025  \n􀀐􀀋􀀋􀀁􀀎􀀄􀀁􀀂 21 A􀀋􀀐􀀋􀀅􀀆 2025  \n􀀎􀀑􀀆􀀒􀀃􀀍􀀓􀀁􀀂 17 S􀀌􀀗􀀆􀀌􀀜􀀏􀀌􀀃 2025  \n􀀋􀀃􀀄􀀐􀀄􀀃􀀌􀀏  \n􀀞􀀄􀀈􀀐 􀀙􀀑 Z􀀂􀀇􀀈􀀐 􀀎􀀑 W􀀌􀀄 L 􀀇􀀈d Z􀀂􀀇􀀈􀀐 􀀊 (2025) A  \n􀀜􀀇 􀀂􀀄􀀈􀀌 􀀘􀀌􀀇􀀃􀀈􀀄􀀈􀀐 􀀇􀀗􀀗􀀃􀀖􀀇 􀀂 􀀛􀀖􀀃 􀀅􀀇dd􀀘􀀌 􀀂􀀌􀀄􀀐􀀂􀀆􀀘􀀇􀀅􀀅􀀄fi 􀀇􀀆􀀄􀀖􀀈 􀀄􀀈 􀀔 􀀘􀀄􀀈􀀐 .  \n􀀙􀀃􀀖􀀈􀀆 . S􀀗􀀖􀀃􀀆􀀅 A 􀀆 . L􀀄􀀓􀀄􀀈􀀐 7:1607212 . d􀀖􀀄: 10.3389/􀀛􀀅􀀗􀀖􀀃 .2025.1607212  \n􀀋􀀌􀀎􀀇􀀈􀀃􀀔􀀓􀀄  \n© 2025 􀀞􀀄􀀈􀀐􀀑 Z􀀂􀀇􀀈􀀐􀀑 W􀀌􀀄 􀀇􀀈d Z􀀂􀀇􀀈􀀐 . T􀀂􀀄􀀅 􀀄􀀅 􀀇􀀈􀀖􀀗􀀌􀀈􀀍􀀇 􀀌􀀅􀀅 􀀇􀀃􀀆􀀄 􀀘􀀌 d􀀄􀀅􀀆􀀃􀀄􀀏􀀋􀀆􀀌d 􀀋􀀈d􀀌􀀃 􀀆􀀂􀀌􀀆􀀌􀀃􀀜􀀅 􀀖􀀛 􀀆􀀂􀀌 􀀁􀀃􀀌􀀇􀀆􀀄􀀓􀀌 􀀁􀀖􀀜􀀜􀀖􀀈􀀅 A􀀆􀀆􀀃􀀄􀀏􀀋􀀆􀀄􀀖􀀈 L􀀄 􀀌􀀈􀀅􀀌 (􀀁􀀁 􀀞Y) . T􀀂􀀌 􀀋􀀅􀀌􀀑 d􀀄􀀅􀀆􀀃􀀄􀀏􀀋􀀆􀀄􀀖􀀈 􀀖􀀃􀀃􀀌􀀗􀀃􀀖d􀀋 􀀆􀀄􀀖􀀈 􀀄􀀈 􀀖􀀆􀀂􀀌􀀃 􀀛􀀖􀀃􀀋􀀜􀀅 􀀄􀀅 􀀗􀀌􀀃􀀜􀀄􀀆􀀆􀀌d􀀑􀀗􀀃􀀖􀀓􀀄d􀀌d 􀀆􀀂􀀌 􀀖􀀃􀀄􀀐􀀄􀀈􀀇􀀘 􀀇􀀋􀀆􀀂􀀖􀀃(􀀅) 􀀇􀀈d 􀀆􀀂􀀌  \n􀀖􀀗􀀔􀀃􀀄􀀐􀀂􀀆 􀀖w􀀈􀀌􀀃(􀀅) 􀀇􀀃􀀌 􀀃􀀌d􀀄􀀆􀀌d 􀀇􀀈d 􀀆􀀂􀀇􀀆 􀀆􀀂􀀌􀀖􀀃􀀄􀀐􀀄􀀈􀀇􀀘 􀀗􀀋􀀏􀀘􀀄 􀀇􀀆􀀄􀀖􀀈 􀀄􀀈 􀀆􀀂􀀄􀀅 j􀀖􀀋􀀃􀀈􀀇􀀘 􀀄􀀅 􀀄􀀆􀀌d􀀑 􀀄􀀈􀀇 􀀖􀀃d􀀇􀀈 􀀌 w􀀄􀀆􀀂 􀀇 􀀌􀀗􀀆􀀌d 􀀇 􀀇d􀀌􀀜􀀄 􀀗􀀃􀀇 􀀆􀀄 􀀌 . N􀀖 􀀋􀀅􀀌􀀑 d􀀄􀀅􀀆􀀃􀀄􀀏􀀋􀀆􀀄􀀖􀀈 􀀖􀀃 􀀃􀀌􀀗􀀃􀀖d􀀋 􀀆􀀄􀀖􀀈 􀀄􀀅􀀗􀀌􀀃􀀜􀀄􀀆􀀆􀀌d w􀀂􀀄 􀀂 d􀀖􀀌􀀅 􀀈􀀖􀀆 􀀖􀀜􀀗􀀘􀀔 w􀀄􀀆􀀂􀀆􀀂􀀌􀀅􀀌 􀀆􀀌􀀃􀀜􀀅 .  \n􀀁 􀀃􀀄􀀅􀀆􀀇􀀈􀀉 􀀊􀀉􀀄􀀋􀀈􀀇􀀈􀀌 􀀄􀀍􀀍􀀋􀀎􀀄􀀅􀀆 􀀏􀀎􀀋􀀐􀀄􀀑􀀑􀀊􀀉 􀀆􀀉􀀇􀀌􀀆􀀒 􀀅􀀊􀀄􀀐􀀐􀀇􀀓􀀅􀀄􀀒􀀇􀀎􀀈  \n􀀇􀀈 􀀅􀀔􀀅􀀊􀀇􀀈􀀌  \n􀀕􀀄􀀈􀀌􀀖􀀎 􀀗􀀇􀀈􀀌 􀀘􀀙 􀀚􀀛􀀎􀀜􀀇􀀈 􀀝􀀆􀀄􀀈􀀌 􀀘􀀙 􀀞􀀇􀀈 􀀛􀀄􀀈 W􀀉􀀇 􀀘 􀀄􀀈􀀑 M􀀇􀀈􀀌 􀀝􀀆􀀄􀀈􀀌 􀀘􀀙2*  \n1 D􀀌􀀗􀀇􀀃􀀆􀀜􀀌􀀈􀀆 􀀖􀀛 􀀞􀀄􀀖􀀜􀀌d􀀄 􀀇􀀘 E􀀈􀀐􀀄􀀈􀀌􀀌􀀃􀀄􀀈􀀐􀀑 􀀙􀀇 􀀋􀀘􀀆􀀔 􀀖􀀛 E􀀈􀀐􀀄􀀈􀀌􀀌􀀃􀀄􀀈􀀐􀀑 T􀀂􀀌 􀀕􀀖􀀈􀀐 K􀀖􀀈􀀐 􀀝􀀖􀀘􀀔􀀆􀀌 􀀂􀀈􀀄􀀒􀀈􀀄􀀓􀀌􀀃􀀅􀀄􀀆􀀔􀀑 􀀕􀀖􀀈􀀐 K􀀖􀀈􀀐 SAR􀀑 􀀁􀀂􀀄􀀈􀀇􀀑 2 R􀀌􀀅􀀌􀀇􀀃 􀀂 I􀀈􀀅􀀆􀀄􀀆􀀋􀀆􀀌 􀀛􀀖􀀃 S􀀗􀀖􀀃􀀆􀀅 􀀇􀀈d T􀀌 􀀂􀀈􀀖􀀘􀀖􀀐􀀔􀀑 T􀀂􀀌 􀀕􀀖􀀈􀀐 K􀀖􀀈􀀐􀀝􀀖􀀘􀀔􀀆􀀌 􀀂􀀈􀀄 􀀒􀀈􀀄􀀓􀀌􀀃􀀅􀀄􀀆􀀔􀀑 􀀕􀀖􀀈􀀐 K􀀖􀀈􀀐 SAR􀀑 􀀁􀀂􀀄􀀈􀀇  \n􀀆􀀕􀀖􀀗􀀘􀀙􀀚􀀛􀀜􀀝􀀞 S􀀇dd􀀘􀀌 􀀂􀀌􀀄􀀐􀀂􀀆 􀀄􀀅 􀀇􀀈 􀀄􀀜􀀗􀀖􀀃􀀆􀀇􀀈􀀆 􀀛􀀇 􀀆􀀖􀀃 􀀄􀀈 􀀏􀀄􀀚􀀌 fi􀀆􀀆􀀄􀀈􀀐 􀀏􀀌 􀀇􀀋􀀅􀀌 􀀄􀀆􀀖􀀃􀀃􀀌􀀘􀀇􀀆􀀌􀀅 w􀀄􀀆􀀂 􀀔 􀀘􀀄􀀈􀀐 􀀌􀀛fi 􀀄􀀌􀀈 􀀔 􀀇􀀈d 􀀆􀀂􀀌 􀀃􀀄􀀅􀀚 􀀖􀀛 􀀄􀀈j􀀋􀀃􀀄􀀌􀀅 . 􀀁􀀖􀀈􀀓􀀌􀀈􀀆􀀄􀀖􀀈􀀇􀀘􀀇􀀗􀀗􀀃􀀖􀀇 􀀂􀀌􀀅 􀀋􀀅􀀌 􀀇􀀈􀀆􀀂􀀃􀀖􀀗􀀖􀀜􀀌􀀆􀀃􀀄 􀀗􀀇􀀃􀀇􀀜􀀌􀀆􀀌􀀃􀀅 􀀇􀀈d j􀀖􀀄􀀈􀀆 􀀇􀀈􀀐􀀘􀀌􀀅 􀀇􀀅 􀀃􀀌􀀛􀀌􀀃􀀌􀀈 􀀌􀀅 􀀆􀀖  \n􀀇􀀘 􀀋􀀘􀀇􀀆􀀌 􀀆􀀂􀀌 􀀖􀀗􀀆􀀄􀀜􀀇􀀘 􀀅􀀇dd􀀘􀀌 􀀂􀀌􀀄􀀐􀀂􀀆􀀑 􀀅􀀋 􀀂 􀀇􀀅 􀀆􀀂􀀌 􀀐􀀃􀀌􀀇􀀆􀀌􀀃 􀀆􀀃􀀖 􀀂􀀇􀀈􀀆􀀌􀀃 􀀂􀀌􀀄􀀐􀀂􀀆 􀀇􀀈d 􀀚􀀈􀀌􀀌 fl􀀌x􀀄􀀖􀀈 􀀇􀀈􀀐􀀘􀀌 . 􀀕􀀖w􀀌􀀓􀀌􀀃􀀑 􀀆􀀂􀀌􀀅􀀌 􀀜􀀌􀀆􀀂􀀖d􀀅 􀀛􀀇􀀄􀀘 􀀆􀀖 􀀖􀀈􀀅􀀄d􀀌􀀃 􀀄􀀈d􀀄􀀓􀀄d􀀋􀀇􀀘 d􀀔􀀈􀀇􀀜􀀄 d􀀄􀀛􀀛􀀌􀀃􀀌􀀈 􀀌􀀅 􀀄􀀈 􀀔 􀀘􀀄􀀈􀀐 .  \n􀀌 je􀀖tive􀀞 T􀀂􀀄􀀅 􀀅􀀆􀀋d􀀔 􀀗􀀃􀀖􀀗􀀖􀀅􀀌d 􀀇 􀀜􀀇 􀀂􀀄􀀈􀀌 􀀘􀀌􀀇􀀃􀀈􀀄􀀈􀀐 (􀀊L) 􀀜􀀖d􀀌􀀘 􀀛􀀖􀀃 􀀇􀀘 􀀋􀀘􀀇􀀆􀀄􀀈􀀐􀀅􀀇dd􀀘􀀌 􀀂􀀌􀀄􀀐􀀂􀀆 􀀏􀀇􀀅􀀌d 􀀖􀀈 􀀌􀀇􀀅􀀄􀀘􀀔 􀀜􀀌􀀇􀀅􀀋􀀃􀀌d 􀀚􀀄􀀈􀀌􀀜􀀇􀀆􀀄 d􀀇􀀆􀀇 .  \nMeth􀀚􀀝􀀞 I􀀈 􀀆􀀖􀀆􀀇􀀘􀀑 16 􀀅􀀋􀀏j􀀌 􀀆􀀅 􀀗􀀇􀀃􀀆􀀄 􀀄􀀗􀀇􀀆􀀌d 􀀄􀀈 􀀃􀀄d􀀄􀀈􀀐 􀀆􀀌􀀅􀀆􀀅 􀀇􀀆 􀀆􀀂􀀃􀀌􀀌 􀀅􀀇dd􀀘􀀌 􀀂􀀌􀀄􀀐􀀂􀀆􀀅 . T􀀂􀀌 􀀜􀀖􀀆􀀄􀀖􀀈 􀀇􀀗􀀆􀀋􀀃􀀌 􀀅􀀔􀀅􀀆􀀌􀀜 􀀃􀀌 􀀖􀀃d􀀌d 􀀆􀀂􀀌 􀀆􀀃􀀇j􀀌 􀀆􀀖􀀃􀀄􀀌􀀅 􀀖􀀛 􀀜􀀇􀀃􀀚􀀌􀀃􀀅 􀀇􀀆􀀆􀀇 􀀂􀀌d 􀀆􀀖􀀆􀀂􀀌􀀄􀀃 􀀘􀀖w􀀌􀀃 􀀘􀀄􀀜􀀏􀀅 . 􀀙􀀌􀀇􀀆􀀋􀀃􀀌􀀅 w􀀌􀀃􀀌 􀀇􀀘 􀀋􀀘􀀇􀀆􀀌d 􀀋􀀅􀀄􀀈􀀐 􀀆􀀂􀀌 􀀂􀀄􀀗􀀑 􀀚􀀈􀀌􀀌􀀑 􀀇􀀈d 􀀇􀀈􀀚􀀘􀀌 j􀀖􀀄􀀈􀀆 􀀇􀀈􀀐􀀘􀀌􀀅 . T􀀂􀀌 􀀖􀀗􀀆􀀄􀀜􀀇􀀘 􀀛􀀌􀀇􀀆􀀋􀀃􀀌 􀀅􀀌􀀆 w􀀇􀀅 􀀅􀀌􀀘􀀌 􀀆􀀌d 􀀋􀀅􀀄􀀈􀀐 􀀛􀀖􀀃w􀀇􀀃d 􀀅􀀌q􀀋􀀌􀀈􀀆􀀄􀀇􀀘􀀛􀀌􀀇􀀆􀀋􀀃􀀌 􀀅􀀌􀀘􀀌 􀀆􀀄􀀖􀀈 . T􀀂􀀌 􀀇 􀀋􀀃􀀇 􀀄􀀌􀀅 􀀖􀀛 􀀛􀀖􀀋􀀃 􀀊L 􀀜􀀖d􀀌􀀘􀀅 w􀀌􀀃􀀌 􀀖􀀜􀀗􀀇􀀃􀀌d 􀀋􀀅􀀄􀀈􀀐􀀘􀀌􀀇􀀓􀀌􀀍􀀖􀀈􀀌􀀍􀀅􀀋􀀏j􀀌 􀀆􀀍􀀖􀀋􀀆 􀀃􀀖􀀅􀀅􀀍􀀓􀀇􀀘􀀄d􀀇􀀆􀀄􀀖􀀈 .  \n􀀈es􀀛lts􀀞 T􀀂􀀌 􀀖􀀗􀀆􀀄􀀜􀀇􀀘 􀀛􀀌􀀇􀀆􀀋􀀃􀀌 􀀅􀀌􀀆 􀀖􀀈􀀆􀀇􀀄􀀈􀀌d 14 􀀛􀀌􀀇􀀆􀀋􀀃􀀌􀀅 􀀃􀀌􀀘􀀇􀀆􀀌d 􀀆􀀖 􀀆􀀂􀀌 􀀂􀀄􀀗􀀑 􀀚􀀈􀀌􀀌􀀑􀀇􀀈d 􀀇􀀈􀀚􀀘􀀌 j􀀖􀀄􀀈􀀆 􀀇􀀈􀀐􀀘􀀌􀀅 . T􀀂􀀌 􀀅􀀇􀀐􀀄􀀆􀀆􀀇􀀘 􀀗􀀘􀀇􀀈􀀌 􀀚􀀈􀀌􀀌 􀀇􀀈􀀐􀀘􀀌 w􀀇􀀅 􀀆􀀂􀀌 􀀜􀀖􀀅􀀆 􀀅􀀌􀀈􀀅􀀄􀀆􀀄􀀓􀀌 􀀆􀀖􀀆􀀂􀀌 􀀅􀀇dd􀀘􀀌 􀀂􀀌􀀄􀀐􀀂􀀆􀀑 w􀀄􀀆􀀂 􀀇 􀀘􀀇􀀅􀀅􀀄fi 􀀇􀀆􀀄􀀖􀀈 􀀇 􀀋􀀃􀀇 􀀔 􀀖􀀛 80% . T􀀂􀀌 􀀁􀀍􀀈􀀌􀀇􀀃􀀌􀀅􀀆 􀀈􀀌􀀄􀀐􀀂􀀏􀀖􀀃􀀜􀀖d􀀌􀀘 􀀂􀀇d 􀀆􀀂􀀌 􀀂􀀄􀀐􀀂􀀌􀀅􀀆 􀀇 􀀋􀀃􀀇 􀀔 􀀖􀀛 99 .79% w􀀂􀀌􀀈 􀀋􀀅􀀄􀀈􀀐 􀀇􀀘􀀘 􀀆􀀂􀀌 􀀖􀀗􀀆􀀄􀀜􀀇􀀘 􀀛􀀌􀀇􀀆􀀋􀀃􀀌􀀅􀀇􀀅 􀀄􀀈􀀗􀀋􀀆􀀅 .  \n􀀋􀀚􀀜􀀖l􀀛si􀀚􀀜􀀞 T􀀂􀀌 􀀗􀀃􀀖􀀗􀀖􀀅􀀌d 􀀜􀀖d􀀌􀀘 􀀖􀀜􀀗􀀌􀀈􀀅􀀇􀀆􀀌􀀅 􀀛􀀖􀀃 􀀆􀀂􀀌 􀀘􀀇 􀀚 􀀖􀀛 􀀖􀀈􀀅􀀄d􀀌􀀃􀀇􀀆􀀄􀀖􀀈 􀀄􀀈􀀆􀀃􀀇d􀀄􀀆􀀄􀀖􀀈􀀇􀀘 􀀜􀀌􀀆􀀂􀀖d􀀅 􀀖􀀛 􀀄􀀈d􀀄􀀓􀀄d􀀋􀀇􀀘 d􀀔􀀈􀀇􀀜􀀄 􀀓􀀇􀀃􀀄􀀇􀀆􀀄􀀖􀀈􀀅 􀀄􀀈 􀀔 􀀘􀀄􀀈􀀐􀀑 􀀗􀀃􀀖􀀓􀀄d􀀄􀀈􀀐 􀀇 􀀜􀀖􀀃􀀌􀀖􀀏j􀀌 􀀆􀀄􀀓􀀌 􀀆􀀖􀀖􀀘 􀀛􀀖􀀃 d􀀇􀀆􀀇􀀍d􀀃􀀄􀀓􀀌􀀈 􀀗􀀌􀀃􀀅􀀖􀀈􀀇􀀘􀀄z􀀇􀀆􀀄􀀖􀀈 􀀄􀀈 􀀏􀀄􀀚􀀌 fi􀀆􀀆􀀄􀀈􀀐 .  \nK􀀁􀀇􀀊􀀌􀀈􀀂􀀍  \n􀀖y􀀖li􀀜􀀘, j􀀚i􀀜t 􀀕􀀜􀀘le, l􀀚we􀀙 lim , m􀀕􀀖hi􀀜e le􀀕􀀙􀀜i􀀜􀀘, s􀀕􀀝􀀝le hei􀀘ht  \n􀀘 I􀀈􀀒􀀋􀀎􀀑􀀛􀀅􀀒􀀇􀀎􀀈  \n􀀁􀀂􀀃􀀄􀀅􀀆􀀇 􀀅􀀉 􀀊􀀋􀀃􀀌􀀍􀀅􀀆􀀇 􀀍􀀌􀀎􀀋 􀀏􀀆􀀐 􀀍􀀌􀀎􀀋 􀀑􀀌􀀑􀀒􀀄􀀏􀀎􀀓 􀀔􀀌􀀕􀀋􀀖􀀋􀀎􀀗 􀀘􀀙􀀋 􀀆􀀒􀀍􀀊􀀋􀀎 􀀌􀀚 􀀌􀀖􀀋􀀎􀀒􀀉􀀋􀀅􀀆􀀛􀀒􀀎􀀅􀀋􀀉 􀀎􀀋􀀄􀀏􀀘􀀋􀀐 􀀘􀀌 􀀃􀀂􀀃􀀄􀀅􀀆􀀇 􀀙􀀏􀀉 􀀏􀀄􀀉􀀌 􀀅􀀆􀀃􀀎􀀋􀀏􀀉􀀋􀀐􀀓 􀀜􀀎􀀌􀀑􀀋􀀎 􀀊􀀅􀀝􀀋 􀀞􀀘􀀘􀀅􀀆􀀇 􀀅􀀉 􀀅􀀍􀀑􀀌􀀎􀀘􀀏􀀆􀀘 􀀘􀀌 􀀎􀀋􀀐􀀒􀀃􀀋􀀘􀀙􀀋 􀀎􀀅􀀉􀀝 􀀌􀀚 􀀅􀀆􀀛􀀒􀀎􀀅􀀋􀀉 􀀏􀀆􀀐 􀀅􀀆􀀃􀀎􀀋􀀏􀀉􀀋 􀀃􀀂􀀃􀀄􀀅􀀆􀀇 􀀋􀀚􀀞􀀃􀀅􀀋􀀆􀀃􀀂 1)􀀓 T􀀎􀀏􀀐􀀅􀀘􀀅􀀌􀀆􀀏􀀄 􀀊􀀅􀀝􀀋 􀀞􀀘􀀘􀀅􀀆􀀇 􀀍􀀋􀀘􀀙􀀌􀀐􀀉􀀎􀀋􀀄􀀂 􀀌􀀆 􀀉􀀘􀀏􀀘􀀅􀀃 􀀍􀀋􀀏􀀉􀀒􀀎􀀋􀀍􀀋􀀆􀀘􀀉􀀗 􀀋􀀍􀀑􀀅􀀎􀀅􀀃􀀏􀀄 􀀎􀀒􀀄􀀋􀀉􀀗 􀀏􀀆􀀐 􀀉􀀒􀀊􀀛􀀋􀀃􀀘􀀅􀀖􀀋 􀀚􀀋􀀋􀀐􀀊􀀏􀀃􀀝 􀀚􀀎􀀌􀀍 􀀃􀀂􀀃􀀄􀀅􀀉􀀘􀀉􀀗􀀕􀀙􀀅􀀃􀀙 􀀍􀀏􀀂 􀀆􀀌􀀘 􀀚􀀒􀀄􀀄􀀂 􀀏􀀃􀀃􀀌􀀒􀀆􀀘 􀀚􀀌􀀎 􀀅􀀆􀀐􀀅􀀖􀀅􀀐􀀒􀀏􀀄 􀀊􀀅􀀌􀀍􀀋􀀃􀀙􀀏􀀆􀀅􀀃􀀏􀀄 􀀖􀀏􀀎􀀅􀀏􀀘􀀅􀀌􀀆􀀉 􀀌􀀎 􀀐􀀂􀀆􀀏􀀍􀀅􀀃􀀎􀀅􀀐􀀅􀀆􀀇 􀀃􀀌􀀆􀀐􀀅􀀘􀀅􀀌􀀆􀀉􀀓 S􀀏􀀐􀀐􀀄􀀋 􀀙􀀋􀀅􀀇􀀙􀀘 􀀅􀀉 􀀌􀀆􀀋 􀀌􀀚 􀀘􀀙􀀋 􀀍􀀌􀀉􀀘 􀀉􀀘􀀒􀀐􀀅􀀋􀀐 􀀖􀀏􀀎􀀅􀀏􀀊􀀄􀀋􀀉 􀀅􀀆 􀀊􀀅􀀝􀀋 􀀞􀀘􀀘􀀅􀀆􀀇􀀊􀀋􀀃􀀏􀀒􀀉􀀋 􀀅􀀘 􀀙􀀏􀀉 􀀏 􀀇􀀎􀀋􀀏􀀘􀀋􀀎 􀀅􀀍􀀑􀀏􀀃􀀘 􀀌􀀆 􀀘􀀙􀀋 􀀎􀀏􀀆􀀇􀀋 􀀌􀀚 􀀍􀀌􀀘􀀅􀀌􀀆 ROM) 􀀌􀀚 􀀘􀀙􀀋 􀀄􀀌􀀕􀀋􀀎 􀀄􀀅􀀍􀀊􀀛􀀌􀀅􀀆􀀘􀀉 􀀏􀀆􀀐 􀀍􀀒􀀉􀀃􀀄􀀋􀀉 􀀘􀀙􀀏􀀆 􀀌􀀘􀀙􀀋􀀎 􀀖􀀏􀀎􀀅􀀏􀀊􀀄􀀋􀀉􀀗 􀀉􀀒􀀃􀀙 􀀏􀀉 􀀙􀀏􀀆􀀐􀀄􀀋􀀊􀀏􀀎 􀀙􀀋􀀅􀀇􀀙􀀘 􀀏􀀆􀀐 􀀃􀀎􀀏􀀆􀀝 􀀄􀀋􀀆􀀇􀀘􀀙 2)􀀓 A 􀀃􀀙􀀏􀀆􀀇􀀋 􀀌􀀚 2% 􀀅􀀆 􀀉􀀏􀀐􀀐􀀄􀀋 􀀙􀀋􀀅􀀇􀀙􀀘 􀀃􀀏􀀆 􀀉􀀅􀀇􀀆􀀅􀀞􀀃􀀏􀀆􀀘􀀄􀀂 􀀏􀀄􀀘􀀋􀀎 􀀄􀀌􀀕􀀋􀀎 􀀄􀀅􀀍􀀊 􀀝􀀅􀀆􀀋􀀍􀀏􀀘􀀅􀀃􀀉􀀗 􀀏􀀚􀀚􀀋􀀃􀀘􀀅􀀆􀀇􀀘􀀙􀀋 􀀋x􀀘􀀋􀀆􀀉􀀅􀀌􀀆 􀀏􀀆􀀐 +􀀋x􀀅􀀌􀀆 􀀏􀀆􀀇􀀄􀀋􀀉 􀀌􀀚 􀀘􀀙􀀋 􀀙􀀅􀀑 􀀏􀀆􀀐 􀀝􀀆􀀋􀀋 􀀛􀀌􀀅􀀆􀀘􀀉 􀀏􀀆􀀐 􀀘􀀙􀀋􀀅􀀎 ROM􀀉 3)􀀓􀀁􀀙􀀏􀀆􀀇􀀋􀀉 􀀅􀀆 􀀉􀀏􀀐􀀐􀀄􀀋 􀀙􀀋􀀅􀀇􀀙􀀘 􀀌􀀚 􀀍􀀌􀀎􀀋 􀀘􀀙􀀏􀀆 4% 􀀃􀀏􀀆 􀀃􀀏􀀒􀀉􀀋 􀀃􀀙􀀏􀀆􀀇􀀋􀀉 􀀅􀀆 􀀌x􀀂􀀇􀀋􀀆 􀀒􀀑􀀘􀀏􀀝􀀋 􀀏􀀆􀀐  \n􀀕􀀋􀀎􀀈􀀒􀀇􀀉􀀋􀀐 􀀇􀀈 S􀀍􀀎􀀋􀀒􀀐 􀀄􀀈􀀑 􀀁􀀅􀀒􀀇v􀀉 􀀞","cbCainhc7d787xiJ","https://ap.wps.com/l/cbCainhc7d787xiJ","pdf",1625609,4,1,11,"English","en",105,"# Introduction\n## Methods\n### Feature extraction\n### Model training and evaluation\n# Results\n## Classification performance\n# Discussion\n## Practical implications","[{\"question\":\"What is the document’s main goal?\",\"answer\":\"To develop a machine learning approach for classifying saddle height in cycling and assess predictive accuracy under the study’s experimental conditions.\"},{\"question\":\"How are inputs represented for classification?\",\"answer\":\"The method uses derived features from cycling-related motion and sensor signals, including variables related to joint angles and motion metrics.\"},{\"question\":\"What performance level is reported?\",\"answer\":\"The document reports high classification performance, including strong agreement between predicted classes and reference labels in the tested setting.\"}]","A machine learning approach for saddle height classification in cycling - research findings | PDF",1785904678,28,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"a-machine-learning-approach-for-saddle-height-classification-in-cycling-research-findings","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/a-machine-learning-approach-for-saddle-height-classification-in-cycling-research-findings/126364/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-18","2026-08-05",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 is the document’s main goal?","Question",{"text":76,"@type":77},"To develop a machine learning approach for classifying saddle height in cycling and assess predictive accuracy under the study’s experimental conditions.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are inputs represented for classification?",{"text":81,"@type":77},"The method uses derived features from cycling-related motion and sensor signals, including variables related to joint angles and motion metrics.",{"name":83,"@type":74,"acceptedAnswer":84},"What performance level is reported?",{"text":85,"@type":77},"The document reports high classification performance, including strong agreement between predicted classes and reference labels in the tested setting.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]