Work

Implementing Real-Time Markerless Motion Capture in Smartphone Exergames

Year: 2025

Type: conference-paper

Abstract: Highlights:• Smartphone cameras using AI-powered pose estimation enable affordable and accessible exergames for rehabilitation and physical activity.• Balancing accuracy and latency is crucial, lightweight AI models improve responsiveness but may reduce movement precision.• User positioning affects visibility and tracking: solutions include larger UI elements, auditory feedback and upper-body-focused exercises.• Optimization of AI models and user studies are needed to improve usability in mobile exergames.

Source: HAL

Authors: Phosanarack, Mathieu, Wallard, Laura, Lepreux, Sophie, Kolski, Christophe, Avril, Eugénie

Institution: Laboratoire d'Automatique, de Mécanique et d'Informatique Industrielles et Humaines

Language: English

Topic: Human Pose and Action Recognition

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7206523993

FWCI:

Cites:

Cited by:

Related to: 10