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Paper Abstract and Keywords
Presentation 2026-06-23 11:15
Estimation of Rider Types from Pedaling Characteristics Using a Pedal-Based Power Meter
Hayato Masuda, Daigo Muramatsu (Seikei Univ.) SIP2026-27 BioX2026-27 IE2026-27 MI2026-27
Abstract (in Japanese) (See Japanese page) 
(in English) In bicycle racing, identifying a rider’s unique style or terrain strengths (rider types) is crucial for tactics and train-ing. Traditionally, these types are classified through physiological lab testing, which imposes high physical and time burdens on general riders. Meanwhile, the prevalence of pedal-based power meters allows for easy acquisition of mechanical data. Since pedaling torque variations strongly reflect muscle recruitment patterns and physical traits, analyzing these time-series data enables the direct estimation of rider characteristics.
This study proposes a method to automatically estimate rider types from real-time torque waveform data. By subdividing a real-world riding course into multiple terrain sections and applying the data to a machine learning algorithm, the proposed approach integrates diverse terrain phases to suppress environmental noise and construct a highly accurate estimation model.
Keyword (in Japanese) (See Japanese page) 
(in English) Rider Types / Pedal-Based Power Meter / Torque Waveform Data / Machine Learning / Real-World Riding Environ- ments / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 84, BioX2026-27, pp. 141-146, June 2026.
Paper # BioX2026-27 
Date of Issue 2026-06-15 (SIP, BioX, IE, MI) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF SIP2026-27 BioX2026-27 IE2026-27 MI2026-27

Conference Information
Committee ITE-IST ITE-ME IE BioX SIP MI  
Conference Date 2026-06-22 - 2026-06-23 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To BioX 
Conference Code 2026-06-IST-ME-IE-BioX-SIP-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Estimation of Rider Types from Pedaling Characteristics Using a Pedal-Based Power Meter 
Sub Title (in English)  
Keyword(1) Rider Types  
Keyword(2) Pedal-Based Power Meter  
Keyword(3) Torque Waveform Data  
Keyword(4) Machine Learning  
Keyword(5) Real-World Riding Environ- ments  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Hayato Masuda  
1st Author's Affiliation Seikei University (Seikei Univ.)
2nd Author's Name Daigo Muramatsu  
2nd Author's Affiliation Seikei University (Seikei Univ.)
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Speaker Author-1 
Date Time 2026-06-23 11:15:00 
Presentation Time 25 minutes 
Registration for BioX 
Paper # SIP2026-27, BioX2026-27, IE2026-27, MI2026-27 
Volume (vol) vol.126 
Number (no) no.83(SIP), no.84(BioX), no.85(IE), no.86(MI) 
Page pp.141-146 
#Pages 6 
Date of Issue 2026-06-15 (SIP, BioX, IE, MI) 

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