Abstract
The present study was aimed at evaluating the Empirical Mode Decomposition (EMD) method to estimate the 3D orientation of the lower trunk during walking using the angular velocity signals generated by a wearable inertial measurement unit (IMU) and notably flawed by drift. The IMU was mounted on the lower trunk (L4-L5) with its active axes aligned with the relevant anatomical axes. The proposed method performs an offline analysis, but has the advantage of not requiring any parameter tuning. The method was validated in two groups of 15 subjects, one during overground walking, with 180° turns, and the other during treadmill walking, both for steady-state and transient speeds, using stereophotogrammetric data. Comparative analysis of the results showed that the IMU/EMD method is able to successfully detrend the integrated angular velocities and estimate lateral bending, flexion-extension as well as axial rotations of the lower trunk during walking with RMS errors of 1 deg for straight walking and lower than 2.5 deg for walking with turns.
Highlights
The last decade has seen the development of inertial measurement units (IMUs) to measure human movement performance [1]
The ability of the Empirical Mode Decomposition (EMD) method to detrend the integrated angular velocity components and to estimate the sensor orientation is depicted in Figure 2, showing the pitch, roll and yaw angles during an entire randomly selected treadmill walking trial
This figure illustrates the quantitative difference between the level of high frequency components, which are contained in the first Intrinsic Mode Function (IMF), and the components identified as drift, i.e., IMF 7 and the final residue
Summary
The last decade has seen the development of inertial measurement units (IMUs) to measure human movement performance [1]. Mass-market electronics companies provide low cost inertial devices, such as those contained in cellphones, that are easy to use, sturdy and small. One of the main advantages is that monitoring movement outside of a laboratory and for a prolonged period of time is possible [2]. With the decrease in size and price, the accuracy of these sensors has correspondingly declined, leading to the main drawback of low cost IMUs, i.e., sensor drift. Sensor drift is a non-linear and low frequency time-varying offset that is due to manufacturing inaccuracies, temperature changes or ageing of electronic components [3]. Drift jeopardizes the time integration of raw signals [4]. The resulting integrated signals, especially for prolonged recordings, can be flawed with a very substantial non-linear trend, hampering their use
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