Recognizing Skateboard and Kickboard Commuting Behaviors Using Activity Trackers: Feasibility Study Using Machine Learning Approaches.

Journal: JMIR formative research
Published Date: Aug 29, 2025

Abstract

BACKGROUND: Active commuting, such as skateboarding and kickboarding, is gaining popularity as an alternative to traditional modes of transportation such as walking and cycling. However, current activity trackers and smartphones, which rely on accelerometer data, are primarily designed to recognize symmetrical locomotive activities (eg, walking and running) and may struggle to accurately identify the unique push-push-glide motion patterns of skateboarding and kickboarding.

Authors

  • Nathanael Aubert-Kato
    Department of Computer Science, Ochanomizu University, Tokyo, Japan.
  • Hitomi Hatori
    Department of Human-Environmental Sciences, Ochanomizu University, Tokyo, Japan.
  • Arisa Orihara
    Department of Human-Environmental Sciences, Ochanomizu University, Tokyo, Japan.
  • Takashi Nakagata
    Center for Physical Activity Research, National Institutes of Biomedical Innovation, Health and Nutrition, Settsu, Japan.
  • Yuji Ohta
    Department of Human-Environmental Sciences, Ochanomizu University, Tokyo, Japan.
  • Julien Tripette
    Center for Interdisciplinary AI and Data Science, Ochanomizu University, 2-1-1 Otsuka, Bunkyo District, Tokyo, 112-8610, Japan, 81 359783032 ext 3032.

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