This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (3278) Machine Learning (2097) Fitness Trackers and Smart Pedometers/Accelerometers (673) Wearable Devices and Sensors (1151)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/71969, first published .
Recognizing Skateboard and Kickboard Commuting Behaviors Using Activity Trackers: Feasibility Study Using Machine Learning Approaches

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

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

Authors of this article:

Nathanael Aubert-Kato1, 2 Author Orcid Image ;   Hitomi Hatori3 Author Orcid Image ;   Arisa Orihara3 Author Orcid Image ;   Takashi Nakagata4, 5 Author Orcid Image ;   Yuji Ohta3 Author Orcid Image ;   Julien Tripette2, 3 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Nathanael Aubert-Kato   1, 2 , PhD ;   Hitomi Hatori   3 , MSc ;   Arisa Orihara   3 , BSc ;   Takashi Nakagata   4, 5 , PhD ;   Yuji Ohta   3 , DEng ;   Julien Tripette   2, 3 , PhD

1 Department of Computer Science, Ochanomizu University, Tokyo, Japan

2 Center for Interdisciplinary AI and Data Science, Ochanomizu University, Tokyo, Japan

3 Department of Human-Environmental Sciences, Ochanomizu University, Tokyo, Japan

4 Center for Physical Activity Research, National Institutes of Biomedical Innovation, Health and Nutrition, Settsu, Japan

5 Laboratory of Gut Microbiome for Health, Microbial Research Center for Health and Medicine, National Institutes of Biomedical Innovation, Health and Nutrition, Settsu, Japan

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