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

The aim of this study is to compare and validate an Inertial Measurement Unit (IMU) relative to an optic system, and to propose methods for pattern recognition to capture behavioural dynamics during sport performance. IMU validation was conducted by comparing the motions of the two arms of a compass, which was equipped with IMUs and reflective landmarks detected by a multi-camera system. Spearman's rank correlation tests showed good correlations between the IMU and multi-camera system, especially when the angles were normalized. Bland-Altman plot, root mean square and the normalized pairwise variability index showed low differences between the two systems, confirming the good accuracy levels of the IMUs. Regarding pattern recognition, joint angle and limb orientation was respectively studied for 25 m during breaststroke swimming and 10 m of indoor rock climbing in athletes of various skill levels. Pattern recognition was also conducted on a macroscopic parameter that captured inter-limb coordination. IMUs revealed the potential to assess movement and coordination variability between and within individuals from joint angle measures in swimming and limb orientation time-series data in climbing.

[1]  Kamiar Aminian,et al.  Front-Crawl Instantaneous Velocity Estimation Using a Wearable Inertial Measurement Unit , 2012, Sensors.

[2]  Melanie Grunwald,et al.  Movement System Variability , 2016 .

[3]  Angelo M. Sabatini,et al.  Estimating Three-Dimensional Orientation of Human Body Parts by Inertial/Magnetic Sensing , 2011, Sensors.

[4]  Qingguo Li,et al.  Inertial Sensor-Based Methods in Walking Speed Estimation: A Systematic Review , 2012, Sensors.

[5]  Wai Yin Wong,et al.  Clinical Applications of Sensors for Human Posture and Movement Analysis: A Review , 2007, Prosthetics and orthotics international.

[6]  William J. McDermott,et al.  Issues in Quantifying Variability From a Dynamical Systems Perspective , 2000 .

[7]  Kamiar Aminian,et al.  Automatic front-crawl temporal phase detection using adaptive filtering of inertial signals , 2013, Journal of sports sciences.

[8]  Ludovic Seifert,et al.  Key Properties of Expert Movement Systems in Sport , 2013, Sports Medicine.

[9]  C Button,et al.  Inter-individual variability in the upper-lower limb breaststroke coordination. , 2011, Human movement science.

[10]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.

[11]  Antonio I Cuesta-Vargas,et al.  The use of inertial sensors system for human motion analysis , 2010, Physical therapy reviews : PTR.

[12]  Frode Eika Sandnes,et al.  Pair-wise Variability Index: Evaluating the Cognitive Difficulty of Using Mobile Text Entry Systems , 2004, Mobile HCI.