Obtaining accurate athletes' centre of mass (CoM) kinematics is crucial for better .... independent between runs, precise magnet positions could therefore be ...
Sport science applied to professional and elite sport - Schneesport
Title The fusion of magneto-inertial sensors with low-cost GNSS can improve the accuracy of determining centre of mass kinematics in alpine ski racing Authors/Affiliation Benedikt Fasel1, Jörg Spörri2, Matthias Gilgien3, Kamiar Aminian1 1 Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; 2Department of Sport Science and Kinesiology, University of Salzburg, HalleinRif, Austria; 3Department of Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway. Abstract Introduction Obtaining accurate athletes’ centre of mass (CoM) kinematics is crucial for better understanding performance and injury risk related aspects of alpine ski racing. For example, in a case study in giant slalom, CoM trajectory differences of 0.1m – 0.5m between the fastest and slowest runs of the same athlete were reported (Spörri, Kröll, Schwameder, & Müller, 2012). The golden standard for estimating CoM trajectories in-field is video-based 3D kinematics, a method based on multiple cameras filming the skier from different perspectives. However, corresponding experimental setups are complex, limited to small capture volumes and due to time-consuming manual digitising efforts not practicable for analysing a large number of runs. Therefore, recently alternative approaches based on global navigation satellite systems (GNSS) have emerged. Since GNSS only records the position of its antenna, for estimating the trajectory of the athlete’s CoM additional processing is needed. For example, Gilgien et al. (2015) estimated CoM trajectory based on a pendulum model and reported an accuracy and precision of 0.09 m and 0.12 m, respectively. Supej (2010) proposed to fuse the GNSS with a full body inertial sensor suit estimating the athlete’s posture, but did not quantitatively validate the system. Fasel, Spörri, Gilgien, et al. (2016) proposed a similar setup and reported an accuracy and precision of 0.08 m and 0.04 m, respectively. All those approaches have in common that they require a professional grade differential GNSS. By the use of differential GNSS, antenna position can be obtained with an accuracy of below 5 cm (Gilgien, Spörri, Limpach, Geiger, & Müller, 2014); however, at the cost of requiring a reference base station. Thus, despite the advantage of allowing for the measurement of a large number of runs, corresponding setups are still relatively time consuming and, therefore, not well suited for routine measurements during daily training. An alternative option to overcome this limitation might be found in replacing the differential GNSS by a regular low-cost GNSS. However, the accuracy of low-cost GNSS of 2.5 m (Gilgien et al., 2014) is not sufficient for skiing measurements. Therefore, the aims of this project were twofold: (i) to extend the work of Fasel, Spörri, & Aminian (2016), and to introduce a novel measurement system that fuses magneto-inertial sensors with the low-cost GNSS; (i) to validate the system in-field against a differential GNSS. The hypothesis of this study was that the accuracy of low-cost GNSS could be improved sufficiently by combination with other independent information sources. Methods Setup of the novel system: Athletes were equipped with inertial sensors (Physilog 4, Gait Up SA, Switzerland) fixed to their shanks, thighs, sacrum, sternum, C7, and head. They recorded acceleration and angular velocity at 500Hz. The sacrum sensor additionally contained a magnetometer (MLX 90393, Melexis, Belgium) sampling at 125Hz. The athletes then wore a customized back protector (P1Dynamic, Ortema, Switzerland) with integrated GNSS antenna (TW2710, Tallysman, Canada) and an additional inertial sensor (Physilog 4) with integrated GNSS chip (u-blox M8, u-blox, Switzerland). Sampling frequency of the GNSS was 10Hz. Finally, a small magnet was buried at each gate of the skiing course. Magnet positions were surveyed with a differential GNSS. Sensor fusion of the novel system: Three independent information sources were fused: 1) a body model with relative CoM obtained from the inertial sensors, 2) the antenna trajectory obtained with the lowcost GNSS, and 3) magnet positions. Fusion took place at the position of the GNSS antenna. Therefore, 56 9. Jahrestagung der SGS in Zürich 9e congrès annuel 4S à Zurich WWW.SPORTWISSENSCHAFT.CH WWW.SCIENCESDUSPORT.CH
Sport science applied to professional and elite sport - Schneesport
in a first step, gravity-free acceleration in the global frame of the GNSS antenna was determined from the inertial sensors fixed to the trunk. For this purpose, sensor orientation was obtained with strapdown integration with motionless drift correction (Favre, Jolles, Siegrist, & Aminian, 2006). Measured acceleration of each sensor was converted to the global frame and the gravity component was removed. Then, each acceleration was translated to the GNSS antenna position. Finally, acceleration of the GNSS antenna was obtained by averaging between all sensors’ accelerations. In a second step, GNSS antenna position at each gate crossing was determined. Each gate crossing was marked by a peak in the magnetic field intensity, caused by the local magnetic field distortion from the buried magnets (Fasel, Spörri, Kröll, & Aminian, 2016). After detecting these peaks the body model from Fasel, Spörri, Gilgien, et al. (2016) was used to estimate the distance between the magnet and GNSS antenna position. To this end it was assumed that the position of the left (right) Antero-Superior Iliac Spine (LASIS) would match the magnet’s position at gate crossing during a left (right) turn. The position of the GNSS antenna was computed as the sum of the absolute magnet position and the distance between LASIS (RASIS) and the GNSS antenna. Information fusion was achieved by applying an Extended Kalman Smoother twice. During the first run, acceleration was fused with GNSS antenna trajectory. During the second run, the magnet positions were added to the filter. Finally, GNSS antenna trajectory was translated to the athlete’s CoM estimated based on the model described in (Fasel, Spörri, Gilgien, et al., 2016). Reference system: The antenna for the differential GNSS (G5Ant-2AT1, Torrance, Canada) was fixed to the athlete’s helmet while the receiver (Alpha-G3T, Javad, San Jose, USA) was worn in a backpack. Reference antenna trajectory was obtained at 10Hz and interpolated to 500Hz using a spline filter. The system was synchronized with the inertial sensors using cross-correlation. Athlete CoM trajectory was estimated using the same body model as with the inertial-sensor based system (Fasel, Spörri, Gilgien, et al., 2016). System comparison: For validation purposes, one European-Cup level athlete skied a 30 gates giant slalom course twice. Accuracy was defined as the mean difference between the proposed system’s CoM trajectory and reference trajectory along each spatial axis. Precision was defined as the standard deviation of this difference. Results For each axis, accuracy and precision for determining a skier´s CoM trajectory were found to be
