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. Introduction 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]. With the growing functionality and ubiquity of smartphones, athletes and coaches have access to an increasingly capable and sophisticated measurement system that includes the phone's inertial measurement unit (three dimensional angular rate gyroscope, accelerometer, and magnetometer) and determinants of location (GPS, GLONASS, etc.). Modern smartphone technology provides position measurements that can be sampled up to about 1 Hzwith stationary absolute accuracy between 0:5 mto16 m and stationary root mean square error (RMSE) November 16, 2019 1/30 between 14 m to 71 m, making them more precise than accurate [2]. The phones also output acceleration and angular velocity data at rates up to about 200 Hz [3]. Competitive rowing aims at maximizing the average boat speed over a specified race distance. For competitions over a typical race distance, the time domain race-to-race variability for elite rowers is approximately 1% and this has been proposed as \an irreducible error for any measure of rowing performance" [4]. However, the discrete unit of action and control in rowing is the stroke and this accordingly represents the domain in which many training and racing parameters are communicated and analyzed. For example, rowing speed is represented in the stroke domain as the product of stroke rate and distance per stroke. In Olympic rowing races, the historical speed difference between finish positions (first and second; second and third; etc.) has averaged at 0.42% [5]. Contextualized in the approximately 200 strokes that it takes to complete a 2000 m race, rowers who generate an additional 5 cmper stroke will ordinarily gain a one place improvement in race finish. Thus, it follows that the accuracy and precision of distance per stroke measurements must be better than 5 cm in order to generate meaningful insight and feedback. Satellite-based positioning systems (GPS, etc.) do not ordinarily afford this level of accuracy and precision thus limiting their effectiveness in the analysis of any individual stroke. We posit that more accurate and precise measures of boat movements for individual strokes will enable a more direct examination of the causal relationships between rower-oar-boat system mechanics and race performance. Therefore this study seeks to improve the accuracy and precision of rowing performance metric measurements. The paper begins with a brief review of the immediately related literature and is followed by an explanation of the problem and statistics used to quantify accuracy and precision of the desired kinematic performance metrics. Two methods are then presented for fusing the smartphone position and motion data to generate more accurate estimates of these metrics. Finally, the estimates are presented against ground truth data collected from a differential GPS (DGPS) system for validation. We close with discussion of the implications and use cases. Related work Real-time water-relative boat speed in rowing has traditionally been measured by either a pitot tube or a small impeller attached to the hull. Modern speedometers make use of GPS receivers to calculate Earth-relative speed and distance in the distance, time, and stroke domains. For example, the popular SpeedCoach GPS (Nielsen Kellerman, Boothwyn, PA, USA) outputs metrics such as boat speed, stroke rate, distance, and elapsed time based on GPS and/or impeller measurements. The accuracy and utility of these systems are limited by the position measurement accuracy and/or the uncertain and frequently fluctuating current velocity. GPS alone has been used to measure position during long distance (15,000 m) rowing events [6] and low cost GPS systems have also been shown to be capable of providing real-time speed estimates during rowing [7]. Other