Automatic front-crawl temporal phase detection using adaptive filtering of inertial signals

Abstract

This study introduces a novel approach for automatic temporal phase detection and inter-arm coordination estimation in front-crawl swimming using inertial measurement units (IMUs). We examined the validity of our method by comparison against a video-based system. Three waterproofed IMUs (composed of 3D accelerometer, 3D gyroscope) were placed on both forearms and the sacrum of the swimmer. We used two underwater video cameras in side and frontal views as our reference system. Two independent operators performed the video analysis. To test our methodology, seven well-trained swimmers performed three 300 m trials in a 50 m indoor pool. Each trial was in a different coordination mode quantified by the index of coordination. We detected different phases of the arm stroke by employing orientation estimation techniques and a new adaptive change detection algorithm on inertial signals. The difference of 0.2 ± 3.9% between our estimation and video-based system in assessment of the index of coordination was comparable to experienced operators' difference (1.1 ± 3.6%). The 95% limits of agreement of the difference between the two systems in estimation of the temporal phases were always less than 7.9% of the cycle duration. The inertial system offers an automatic easy-to-use system with timely feedback for the study of swimming.

Assessment of three-dimensional joint kinematics of the upper limb during simulated swimming using wearable inertial-magnetic measurement units

“…Recently, wearable inertial-magnetic measurement units (IMMUs) have been used (Magalhaes, Vannozzi, Gatta, & Fantozzi, 2015). Initially, stroke rate, stroke phases analysis and discrimination among different swimming styles were performed (Dadashi et al, 2013;Le Sage et al, 2011;Ohgi, Ichikawa, Homma, & Miyaji, 2003). Subsequently, the velocity, attitude and position of the swimmer were estimated to have specific performance indicators for the whole length of the swimming pool (Dadashi, Crettenand, Millet, & Aminian, 2012;Stamm, James, & Thiel, 2013).…”

Pattern Recognition in Cyclic and Discrete Skills Performance from Inertial Measurement Units

“…IMUs have also been used to identify swimming stroke phases (Dadashi et al, 2013). Understanding of the potential role of IMUs could be gained by analysing inter-limb coordination pattern variability during performance of complex multi-articular activities under varying task constraints.…”

Behavioral Dynamics in Swimming: The Appropriate Use of Inertial Measurement Units

“…Yet these analyses remain limited for evaluating higher-order parameters, which require another level of investigation (Callaway et al, 2009; de Magalhães et al, 2014). For this purpose, researchers turned to three-dimensional optoelectronic analyses (Chiari et al, 2005) based on the automatic detection of reflective markers positioned on swimmers’ joints to properly track their motion (Callaway et al, 2009; Dadashi et al, 2013c). For an example in breaststroke, consider the real-time data collected in a calibrated volume by Olstad et al (2012).…”

“…This method is the gold standard in laboratory conditions, but remains relatively rare outdoors or in constrained environments, such as underwater (Silvatti et al, 2012; de Jesus et al, 2015). Another major issue in swimming is that the analyses are performed over a restricted area (Ceccon et al, 2013) of only three or four stroke cycles (Dadashi et al, 2013c; Callaway, 2015). This means that, although multi-camera systems can be used for inter-individual or intra-cyclic analyses of high-order movement parameters, they are of limited use for investigating behavioral dynamics.…”