Abstract
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.
Highlights
Non-intrusive collection of data from athletes during practice and competition provides opportunities for evidenced-based performance evaluation and coaching
We have presented two methods to estimate the distance, speed, and distance per stroke along a rowing boat’s path in real time that provide improved accuracy and precision results from the relatively low accuracy sensors in a single smartphone attached to the boat
We show that the distance per stroke can be estimated to an accuracy and precision of about 50 cm, which is an improvement over smartphone estimates but still insufficient for detailed stroke-by-stroke level differentiation of boats in a racing event with relatively close elapsed times
Summary
Non-intrusive collection of data from athletes during practice and competition provides opportunities for evidenced-based performance evaluation and coaching. Traditional kinematic measurement techniques in sports have frequently required elaborate equipment to capture the motion of human body segments and associated sports equipment; see examples in [1]. Paid any salaries to any of the authors, including RPC, and did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of author RPC are articulated in the “author contributions" section
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