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Body-worn sensors for movement analysis in swimming have to be unobtrusive and energy-efficient. We present a swimming exercise tracker for the unobtrusive positioning at the back of the head and an energy-efficient analysis using an on-node implementation. To develop the system, we collected head kinematics from 11 subjects in two 200-m medley races comprising breaks, turns, and four swimming styles. Each subject was equipped with a 6-D inertial measurement unit and completed one session in rested and fatigued state. Data were analyzed with a classification system, whereby different classifiers, window sizes, and feature sets were evaluated. Algorithm selection for on-node processing was performed on the basis of classifier accuracy and computational cost. The algorithm with the best tradeoff in accuracy and computational cost was selected and had a classification rate of 85.4%. Energy …