Related Experiment Video
Updated: Jan 2, 2026

06:52
An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
Published on: May 26, 2020
8.4K
Adaptive smartphone-based sensor fusion for estimating competitive rowing kinematic metrics.
Bryn Cloud1, Britt Tarien1, Ada Liu1
1Mechanical and Aerospace Engineering, University of California Davis, Davis, California, United States of America.
Plos One
|December 6, 2019
Summary
This study introduces two digital filters that use smartphone sensors to accurately measure rowing boat speed and distance. These filters significantly improve performance metrics compared to GPS alone.
Area of Science:
- Sports Science
- Engineering
- Data Analysis
Background:
- Competitive rowing relies on precise boat position and velocity data for performance analysis.
- Smartphones offer accessible GPS and accelerometer sensors for potential rowing applications.
Purpose of the Study:
- To investigate the efficacy of two real-time digital filters for accurate boat speed and distance measurement in rowing.
- To assess the performance improvements offered by complementary and Kalman filters compared to smartphone GPS alone.
Main Methods:
- Developed and implemented a complementary filter and a Kalman filter integrating smartphone acceleration and location data.
- Validated filter performance against a high-precision differential GPS system.
- Conducted experiments with two rowers in different boats over a 300m course.
Main Results:
- The complementary filter enhanced boat speed, distance, and distance per stroke accuracy by 44%, 42%, and 73%, respectively.
- The Kalman filter improved boat speed, distance, and distance per stroke accuracy by 48%, 22%, and 82%, respectively.
- Both filters demonstrated substantial improvements over single-channel GPS measures.
Conclusions:
- Real-time digital filters utilizing smartphone sensors offer a cost-effective solution for enhancing rowing performance metrics.
- These filtering techniques show significant promise for improving the accuracy and precision of boat speed and distance estimations.
- The developed filters can be valuable tools for real-time feedback, training, and post-session analysis in rowing.

