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

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 work, we present the use of two real-time digital filters to achieve highly accurate yet reasonably priced measurements of boat speed and distance traveled per stroke. 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.