Three-Dimensional Body and Centre of Mass Kinematics in Alpine Ski Racing Using Differential GNSS and Inertial Sensors

A key point in human movement analysis is measuring the trajectory of a person’s center of mass (CoM). For outdoor applications, differential Global Navigation Satellite Systems (GNSS) can be used for tracking persons since they allow measuring the trajectory and speed of the GNSS antenna with centimeter accuracy. However, the antenna cannot be placed exactly at the person’s CoM, but rather on the head or upper back. Thus, a model is needed to relate the measured antenna trajectory to the CoM trajectory. In this paper we propose to estimate the person’s posture based on measurements obtained from inertial sensors. From this estimated posture the CoM is computed relative to the antenna position and finally fused with the GNSS trajectory information to obtain the absolute CoM trajectory. In a biomechanical field experiment, the method has been applied to alpine ski racing and validated against a camera-based stereo photogrammetric system. CoM position accuracy and precision was found to be 0.08 m and 0.04 m, respectively. CoM speed accuracy and precision was 0.04 m/s and 0.14 m/s, respectively. The observed accuracy and precision might be sufficient for measuring performance- or equipment-related trajectory differences in alpine ski racing. Moreover, the CoM estimation was not based on a movement-specific model and could be used for other skiing disciplines or sports as well.

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Kamiar Aminian, Geo Boffi, Julien Chardonnens, Benedikt Fasel
MDPI, 2016
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https://infoscience.epfl.ch/record/220935?ln=en

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Inertial measurement unit

An inertial measurement unit (IMU) is an electronic device that measures and reports a body's specific force, angular rate, and sometimes the orientation of the body, using a combination of acceler

Kinematics

Kinematics is a subfield of physics, developed in classical mechanics, that describes the motion of points, bodies (objects), and systems of bodies (groups of objects) without considering the forces t

Speed

In everyday use and in kinematics, the speed (commonly referred to as v) of an object is the magnitude of the change of its position over time or the magnitude of the change of its position per unit

Determination of the centre of mass kinematics in alpine skiing using differential global navigation satellite systems

Julien Chardonnens

In the sport of alpine skiing, knowledge about the centre of mass (CoM) kinematics (i.e. position, velocity and acceleration) is essential to better understand both performance and injury. This study proposes a global navigation satellite system (GNSS)-based method to measure CoM kinematics without restriction of capture volume and with reasonable set-up and processing requirements. It combines the GNSS antenna position, terrain data and the accelerations acting on the skier in order to approximate the CoM location, velocity and acceleration. The validity of the method was assessed against a reference system (video-based 3D kinematics) over 12 turn cycles on a giant slalom skiing course. The mean (+/- s) position, velocity and acceleration differences between the CoM obtained from the GNSS and the reference system were 9 +/- 12cm, 0.08 +/- 0.19m center dot s(-1) and 0.22 +/- 1.28m center dot s(-2), respectively. The velocity and acceleration differences obtained were smaller than typical differences between the measures of several skiers on the same course observed in the literature, while the position differences were slightly larger than its discriminative meaningful change. The proposed method can therefore be interpreted to be technically valid and adequate for a variety of biomechanical research questions in the field of alpine skiing with certain limitations regarding position.

Taylor & Francis2015

Estimation of the centre of mass kinematics in alpine ski racing using inertial and magnetic sensors

Kamiar Aminian, Benedikt Fasel

For performance analysis in alpine ski racing, an accurate and precise estimation of the centre of mass (CoM) kinematics is indispensable. Currently available systems satisfying this need are video-based stereo-photogrammetry or differential global navigation satellite systems (GNSS). However, they are impractical to use in regular training settings. Inertial sensors could be used instead but suffer from significant drifts in speed and position estimation due to the integration of acceleration and angular velocity data. The aim of the present study was to propose and validate an inertial and magnetometer sensor-based algorithm to estimate CoM kinematics in alpine ski racing. Relative CoM position and speed between-run comparisons were found to be highly accurate and precise with mean absolute deviations

2017

Real-world gait speed estimation using wrist sensor: A personalized approach

Kamiar Aminian, Hooman Dejnabadi, Martin Savary, Abolfazl Soltani

Gait speed is an important parameter to characterize people's daily mobility. For real-world speed measurement, inertial sensors or Global Navigation Satellite System (GNSS) can be used on wrist, possibly integrated in a wristwatch. However, power consumption of GNSS is high and data are only available outdoor. Gait speed estimation using wrist-mounted inertial sensors is generally based on machine learning and suffers from low accuracy due to the inadequacy of using limited training data to build a general speed model that would be accurate for the whole population. To overcome this issue, a personalized model was proposed, which took unique gait style of each subject into account. Cadence and other biomechanically-derived gait features were extracted from wrist-mounted accelerometer and barometer. Gait features were fused with few GNSS data (sporadically sampled during gait) to calibrate the step length model of each subject through online learning. The proposed method was validated on 30 healthy subjects. For walking, it has achieved a median [Interquartile Range] RMSE, bias and precision of 0.05 [0.04-0.06], 0.00 [-0.01 0.00], and 0.06 [0.05 0.07] (m/s), respectively. For running, the errors are 0.14 [0.11 0.17], 0.00 [-0.01 0.02], and 0.18 [0.14 0.23] (m/s), respectively. Results demonstrated that the personalized model provided similar performance as GNSS. It used 50 times less training GNSS data than non-personalized method and achieved even better results. This parsimonious GNSS usage allowed extending battery life. The proposed algorithm met requirements for applications which need accurate, long, real-time, low-power, and indoor/outdoor speed estimation in daily life.

2019