Optimizing the Interaction System for Treadmill Video Games Using a Smartphone’s Front Camera

This paper introduces a lightweight and accessible interaction system for treadmill-based video games, relying solely on facial tracking via a smartphone's front camera. The system enables real-time estimation of running cadence and directional control through natural head movements, providing an immersive and hands-free gaming experience. A key contribution is the implementation of a FFT-based cadence estimation method that achieves accuracy errors below 5% using only 128 frames, enabling real-time feedback. Preliminary evaluations with 11 participants demonstrate that the FFT-based approach outperforms traditional peak detection in both accuracy and robustness across multiple running speeds. These results position the system as a practical, efficient, and scalable solution for fitness-oriented human-computer interaction, with promising implications for digital health and exergaming.

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Publication Details

Journal
Sensors
Published
2025-12-19
DOI
https://doi.org/10.3390/s26010020
Primary Topic
Gaze Tracking and Assistive Technology
Type
article
Field-Weighted Citation Impact
0.00

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article

Optimizing the Interaction System for Treadmill Video Games Using a Smartphone’s Front Camera

Miguel Chover, Carlos Marín-Lora, Micaela Martin, María Beatriz Villar-López
Sensors
Gaze Tracking and Assistive Technology
article

Optimizing the Interaction System for Treadmill Video Games Using a Smartphone’s Front Camera

Miguel Chover, Carlos Marín-Lora, Micaela Martin, María Beatriz Villar-López
article en

Abstract

This paper introduces a lightweight and accessible interaction system for treadmill-based video games, relying solely on facial tracking via a smartphone's front camera. The system enables real-time estimation of running cadence and directional control through natural head movements, providing an immersive and hands-free gaming experience. A key contribution is the implementation of a FFT-based cadence estimation method that achieves accuracy errors below 5% using only 128 frames, enabling real-time feedback. Preliminary evaluations with 11 participants demonstrate that the FFT-based approach outperforms traditional peak detection in both accuracy and robustness across multiple running speeds. These results position the system as a practical, efficient, and scalable solution for fitness-oriented human-computer interaction, with promising implications for digital health and exergaming.

SensorsVol. 26(1)
Universitat Jaume I (ES), Universidad de Zaragoza (ES)
European Regional Development Fund, Agencia Estatal de Investigación
Openalex Percentile: Top 57%
Gaze Tracking and Assistive Technology
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