Adaptive smartphone-based sensor fusion for estimating competitive rowing kinematic metrics

Autoři: Bryn Cloud aff001;  Britt Tarien aff001;  Ada Liu aff001;  Thomas Shedd aff001;  Xinfan Lin aff001;  Mont Hubbard aff001;  R. Paul Crawford aff002;  Jason K. Moore aff001
Působiště autorů: Mechanical and Aerospace Engineering, University of California Davis, Davis, California, United States of America aff001;  Hegemony Technologies LLC, Davis, California, United States of America aff002
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
prolekare.web.journal.doi_sk: 10.1371/journal.pone.0225690


Competitive rowing highly values boat position and velocity data for real-time feedback during training, racing and post-training analysis. The ubiquity of smartphones with embedded position (GPS) and motion (accelerometer) sensors motivates their possible use in these tasks. In this paper, we investigate the use of two real-time digital filters to achieve highly accurate yet reasonably priced measurements of boat speed and distance traveled. Both filters combine acceleration and location data to estimate boat distance and speed; the first using a complementary frequency response-based filter technique, the second with a Kalman filter formalism that includes adaptive, real-time estimates of effective accelerometer bias. The estimates of distance and speed from both filters were validated and compared with accurate reference data from a differential GPS system with better than 1 cm precision and a 5 Hz update rate, in experiments using two subjects (an experienced club-level rower and an elite rower) in two different boats on a 300 m course. Compared with single channel (smartphone GPS only) measures of distance and speed, the complementary filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 44%, 42%, and 73%, respectively, while the Kalman filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 48%, 22%, and 82%, respectively. Both filters demonstrate promise as general purpose methods to substantially improve estimates of important rowing performance metrics.

Klíčová slova:

Acceleration – Accelerometers – Boats – Cell phones – Kalman filter – Kinematics – Signal filtering – Sports


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