Related Experiment Video
Updated: Nov 6, 2025
Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights
Published on: October 25, 2024
Mobile Motion Tracking for Disease Prevention and Rehabilitation Using Apple ARKit
Lara Marie Reimer1, Severin Weigel1, Florian Ehrenstorfer1
1Technical University of Munich, Munich, Germany.
Background:
Physical activity helps improve the overall quality of life. The correct execution of physical activity is crucial both in sports as well as disease prevention and rehabilitation. Little to no automated commodity solutions for automated analysis and feedback exist.
Objectives:
Validation of the Apple ARKit framework as a solution for automatic body tracking in daily physical exercises using the smartphones' built-in camera.
Methods:
We deliver insights into ARKit's body tracking accuracy through a lab experiment against the VICON system as Gold Standard. We provide further insights through case studies using apps built on ARKit.
Results:
ARKit exposes significant limitations in tracking the full range of motion in joints but accurately tracks the movement itself. Case studies show that applying it to measure the quantity of execution of exercises is possible.
Conclusion:
ARKit is a light-weight commodity solution for quantitative assessment of physical activity. Its limitations and possibilities in qualitative assessment need to be investigated further.
