Detecting repetitions and time features in resistance training using triaxial accelerometry

Accurately determining resistance-training parameters is crucial to gain knowledge about the training process and to evaluate training interventions. To current knowledge, no method exists to automatically detect a series of features in a repetition training session using one three-dimensional accelerometer. In this study, a specific algorithm was used to detect the number of repetitions and different time features. Features determined by the acceleration algorithm were compared to a reference system using a linear wire encoder to detect movements. A total of 50 healthy participants were randomly assigned to three different groups (maximal strength, hypertrophy, and muscular endurance) and executed three different resistance-training exercises (bench press, leg press, and trunk flexion). Results of both measurement systems were compared for agreement using Bland–Altman plots, regarding repetition numbers, TUT (time under tension) in concentric, eccentric, and isometric contraction forms and total TUT (sum of all contraction forms) as well as break between repetitions. Both methods showed high agreement in repetition count (mean error − 0.2 ± 0.6 repetitions). Time features were detected with less agreement, with 10.0% disagreement for TUT in first phase, 1.1% disagreement for second phase, and 56% disagreement in isometric contraction. However, it was possible to detect a series of time-based movement features, enhancing the possibility to objectively record different parameters of a resistance training session. This will improve research in resistance training and also bring advantages in the training process for coach and athlete.