Abstract
The present study investigates an algorithm for the calculation of 3D joint angles based on inertial measurement units (IMUs), omitting magnetometer data. Validity, test-retest reliability, and long-term stability are evaluated in reference to an optical motion capture (OMC) system. Twenty-eight healthy subjects performed a 6 min walk test. Three-dimensional joint kinematics of the lower extremity was recorded simultaneously by means of seven IMUs and an OptiTrack OMC system. To evaluate the performance, the root mean squared error (RMSE), mean range of motion error (ROME), coefficient of multiple correlations (CMC), Bland-Altman (BA) analysis, and intraclass correlation coefficient (ICC) were calculated. For all joints, the RMSE was lower than 2.40°, and the ROME was lower than 1.60°. The CMC revealed good to excellent waveform similarity. Reliability was moderate to excellent with ICC values of 0.52–0.99 for all joints. Error measures did not increase over time. When considering soft tissue artefacts, RMSE and ROME increased by an average of 2.2° ± 1.5° and 2.9° ± 1.7°. This study revealed an excellent correspondence of a magnetometer-free IMU system with an OMC system when excluding soft tissue artefacts.
Highlights
Marker-based optical motion capture (OMC) systems are commonly used in clinical movement analysis [1] and are considered the gold standard
Combining more inertial measurement units (IMUs) attached to linked body segments, it is possible to estimate the joint kinematics of the specified segments [1,4,5]
The poorest outcome concerning the root mean squared error (RMSE) was evident in knee rotation (1.75◦ –2.34◦ ) and knee abduction for range of motion error (ROME) (1.11◦ –1.58◦ )
Summary
Marker-based optical motion capture (OMC) systems are commonly used in clinical movement analysis [1] and are considered the gold standard. Body-worn inertial measurement units (IMUs) present a mobile alternative [1]. IMUs incorporate 3D accelerometers, 3D gyroscopes, and, typically, 3D magnetometers, measuring 3D linear acceleration, 3D angular velocity, and 3D magnetic field, respectively. E.g., variations of the Kalman filter or optimization based methods [2], it is possible to estimate the IMUs’ orientation in reference to a global coordinate system [3]. Combining more IMUs attached to linked body segments, it is possible to estimate the joint kinematics of the specified segments [1,4,5]
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