Comparison of an IMU- and magnet timing gate- based system and an opto-electronic device for analysing running biomechanics

Abstract Nowadays, wearable sensors stand out as an advantageous alternative to lab-based assessments for running gait analysis. Due to their advantages, they could be used more easily under real-life conditions in the athlete’s natural training and competition environment. However, empirical evidence about their accordance with other established systems (such as force plates or opto-electronic systems) is still rare. The objective of this study was to evaluate the accordance of an Inertial Measurement Unit- and magnetic timing gate- based system and an opto-electronical device for analysing biomechanical performance parameters during running with different speeds on an outdoor synthetic track. A total of 14 participants ran over nine laps at three different running speeds while wearing wearable sensors attached to the shin and the lower back. An opto-electronical device served as a reference measure for capturing ground contact time, step length and step frequency. The statistical analysis included descriptive and Bland–Altman statistics, Pearson correlation and repeated measures ANOVA. Results reveal a systematic difference in ground contact time estimation of 21 ± 18.4 ms, with Bland–Altman Limits of Agreement ranging from −19.1 to 53.0 ms and a significant ( p < 0.001) effect of running speed. In the comparison of step lengths between both systems, there was an absolute mean difference of 0.01 m, with upper and lower Bland–Altman Limits of Agreement of ± 0.016 m. The mean difference for step frequencies was 0 ± 0.02 Hz. No effects of running speed were observed for step length and frequency estimation. Overall, the ability of the IMU- and magnetic timing gate-based system to record data under ecological valid conditions during training or competition is promising. Further improvements in extraction algorithms, especially personalised ones, may lead to more accurate parameter extraction, allowing comprehensive performance analysis.