Abstract
A Drift-Free 3D Orientation and Displacement estimation method (DFOD) based on a single inertial measurement unit (IMU) is proposed and validated. Typically, body segment orientation and displacement methods rely on a constant- or zero-velocity point to correct for drift. Therefore, they are not easily applicable to more proximal segments than the foot. DFOD uses an alternative single sensor drift reduction strategy based on the quasi-cyclical nature of many human movements. DFOD assumes that the quasi-cyclical movement occurs in a quasi-2D plane and with an approximately constant cycle average velocity. DFOD is independent of a constant- or zero-velocity point, a biomechanical model, Kalman filtering or a magnetometer. DFOD reduces orientation drift by assuming a cyclical movement, and by defining a functional coordinate system with two functional axes. These axes are based on the mean acceleration and rotation axes over multiple complete gait cycles. Using this drift-free orientation estimate, the displacement of the sensor is computed by again assuming a cyclical movement. Drift in displacement is reduced by subtracting the mean value over five gait cycle from the free acceleration, velocity, and displacement. Estimated 3D sensor orientation and displacement for an IMU on the lower leg were validated with an optical motion capture system (OMCS) in four runners during constant velocity treadmill running. Root mean square errors for sensor orientation differences between DFOD and OMCS were 3.1 ± 0.4° (sagittal plane), 5.3 ± 1.1° (frontal plane), and 5.0 ± 2.1° (transversal plane). Sensor displacement differences had a root mean square error of 1.6 ± 0.2 cm (forward axis), 1.7 ± 0.6 cm (mediolateral axis), and 1.6 ± 0.2 cm (vertical axis). Hence, DFOD is a promising 3D drift-free orientation and displacement estimation method based on a single IMU in quasi-cyclical movements with many advantages over current methods.
Highlights
Activities like walking, running, swimming, rowing, and skating are all quasi-cyclical in nature
This opens up new possibilities of analyzing movements that are difficult to measure in a lab, due to technical constraints of optical motion capture systems (OMCS)
Estimated lower leg sensor orientations of Drift-Free Orientation and Displacement estimation (DFOD) were compared to an OMCS in treadmill running
Summary
Activities like walking, running, swimming, rowing, and skating are all quasi-cyclical in nature The repetitiveness of these movements, and their associated loads on the human body, can result in overuse injuries [1]. With the introduction of wearable systems, motion analysis is no longer restricted to a controlled lab setting [2,3]. This opens up new possibilities of analyzing movements that are difficult to measure in a lab, due to technical constraints of optical motion capture systems (OMCS). The acceleration, orientation, and displacement of a sensor are of interest for many motion analysis applications such as impact analyses, monitoring the range of motion (ROM), or inclination of a body segment, e.g., the lower leg [5,6]. Drift can alternatively be reduced by applying domain-specific assumptions, such as the zero-velocity update method [9,10]
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