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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

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

Nathanael Aubert-Kato^1,2, Hitomi Hatori³, Arisa Orihara³

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

JMIR Formative Research

|August 29, 2025

Summary

This summary is machine-generated.

Wrist-worn sensors can accurately detect skateboarding and kickboarding, offering a new way to track active commuting. This push-push-glide activity recognition shows promise for fitness trackers.

Keywords:

activity recognition machine learning physical activity skateboard wearable sensor

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Area of Science:

Wearable technology
Human-computer interaction
Biomechanics

Background:

Active commuting methods like skateboarding and kickboarding are growing in popularity.
Current activity trackers struggle to recognize the unique motion patterns of these activities.

Purpose of the Study:

To assess the feasibility of classifying skateboarding and kickboard commuting using wearable sensors and smartphones.
To identify key sensor-derived features for accurate activity recognition.

Main Methods:

Ten participants performed nine activities, including skateboarding and kickboarding.
Data collected from wrist, hip, and pocket sensors (accelerometer, gyroscope, barometer).
Random forest classifiers used for multiclass and binary classifications.

Main Results:

Wrist-worn sensors achieved the highest accuracies (84%-88%) for multiclass classification.
High sensitivity for identifying skateboarding (93%-99%) and kickboarding (97%-99%).
Push-push-glide superclass classification achieved high accuracies (91%-95%).

Conclusions:

Recognizing skateboard and kickboard commuting is feasible with wearable sensors, especially wrist-worn devices.
Broader push-push-glide classification shows potential for activity tracker integration.
Future research should focus on sensor fusion and energy expenditure estimation.