Vibration Data Mining and Machine Learning for Anomaly Detection of Cycling Trails Using Instrumented Bike

Chun Hsing Ho, Kewei Ren

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The instrumented bike has been seen as a useful tool for condition surveys of cycling trails. This paper introduces the recent development of instrumented bike and its application in quality assessment of cycling trails through cycling data collection, data mining/processing, data analysis, anomaly detection and mapping. A machine learning-based computing algorithm using LSTM method is presented to demonstrate how vibration patterns are screened and anomalies are identified. A cycling test site was selected at Northern Arizona University, USA. Four cyclists were recruited to participate in the cycling data collection using their individual instrumented bike. The results show the LSTM method is capable of analyzing vibration patterns and identifying anomalies along the cycling trails. The computing algorithm is suitable in the development of instrumented bike.

Original languageEnglish (US)
Title of host publication2024 9th International Conference on Big Data Analytics, ICBDA 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages123-127
Number of pages5
ISBN (Electronic)9798350352962
DOIs
StatePublished - 2024
Externally publishedYes
Event9th International Conference on Big Data Analytics, ICBDA 2024 - Hybrid, Tokyo, Japan
Duration: Mar 16 2024Mar 18 2024

Publication series

Name2024 9th International Conference on Big Data Analytics, ICBDA 2024

Conference

Conference9th International Conference on Big Data Analytics, ICBDA 2024
Country/TerritoryJapan
CityHybrid, Tokyo
Period3/16/243/18/24

Keywords

  • anomaly detection
  • cycling trails
  • instrumented bike
  • machine learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Modeling and Simulation
  • Health Informatics

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