Abstract
In human movement analysis, 3D body segment orientation can be obtained through the numerical integration of gyroscope signals. These signals, however, are affected by errors that, for the case of micro-electro-mechanical systems, are mainly due to: constant bias, scale factor, white noise, and bias instability. The aim of this study is to assess how the orientation estimation accuracy is affected by each of these disturbances, and whether it is influenced by the angular velocity magnitude and 3D distribution across the gyroscope axes. Reference angular velocity signals, either constant or representative of human walking, were corrupted with each of the four noise types within a simulation framework. The magnitude of the angular velocity affected the error in the orientation estimation due to each noise type, except for the white noise. Additionally, the error caused by the constant bias was also influenced by the angular velocity 3D distribution. As the orientation error depends not only on the noise itself but also on the signal it is applied to, different sensor placements could enhance or mitigate the error due to each disturbance, and special attention must be paid in providing and interpreting measures of accuracy for orientation estimation algorithms.
Highlights
One of the main issues in human movement analysis is the accurate estimation of the three-dimensional (3D) orientation of a body segment, relative to a global Earth-fixed reference frame
The differences in the angular velocity observed at the pelvis and shank during a stride are shown in Figure 2, both in terms of magnitude and 3D distribution
Regarding the 3D distribution (Figure 2b), the trajectory followed by the unit vector of the angular velocity in the pelvis and shank is reported, as well as the centroids of the two trajectories: their direction represents the mean direction of the angular velocity and their norm reflects how dispersed the angular velocity distribution is during a stride [24]
Summary
One of the main issues in human movement analysis is the accurate estimation of the three-dimensional (3D) orientation of a body segment, relative to a global Earth-fixed reference frame. The integration process leads to errors that grow over time and, in addition, the initial conditions for the integration often need to be determined. In order both to reduce error propagation and to obtain the integration process initial conditions, a sensor fusion approach is often followed [9]. In such cases, the gyroscope signal still represents the basis for MIMU orientation estimation, but its information is refined with the data from the accelerometers and magnetic sensors
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