A method for deriving displacement data during cyclical movement using an inertial sensor.

Abstract

Biomechanical studies often employ optical motion capture systems for the determination of the position of an object in a room-based coordinate system. This is not ideal for many types of study in locomotion since only a few strides may be collected per ;trial', and outdoor experiments are difficult with some systems. Here, we report and evaluate a novel approach that enables the user to determine linear displacements of a proprietary orientation sensor during cyclical movement. This makes experiments outside the constraints of the laboratory possible, for example to measure mechanical energy fluctuations of the centre of mass during over-ground locomotion. Commercial orientation sensors based on inertial sensing are small and lightweight and provide a theoretical framework for determining position from acceleration. In practice, the integration process is difficult to implement because of integration errors, integration constants and the necessity to determine the orientation of the measured accelerations. Here, by working within the constraints of cyclical movements, we report and evaluate a method for determining orientation and relative position using a modified version of a commercial inertial orientation sensor that combines accelerometers, gyroscopes and magnetometers, thus giving a full set of movement parameters (displacement, velocity and acceleration in three dimensions). The 35 g sensor was attached over the spine of a horse exercising on a treadmill. During canter locomotion (9.0 m s-1), the amplitudes of trunk movement in the x (craniocaudal), y (mediolateral) and z (dorsoventral) directions were 99.6, 57.9 and 140.2 mm, respectively. Comparing sensor displacement values with optical motion capture values for individual strides, the sensor had a median error (25th, 75th percentile) in the x, y and z directions of 0.1 (-9.7, +10.8), -3.8 (-15.5, +13.7) and -0.1 (-6.3, +7.1) mm, respectively. High-pass filtering of the displacement data effectively separated non-cyclical from cyclical components of the movement and reduced the interquartile ranges of the errors considerably to (-3.6, 6.2), (-4.0, 3.8) and (-4.5, 5.1) for x, y and z displacement, respectively, during canter locomotion. This corresponds to (-3.2, 5.5)%, (-6.7, 6.3)% and (-3.3, 3.7)% of the range of motion.