Automatic anatomical calibration for IMU-based elbow angle measurement in disturbed magnetic fields

Abstract

Inertial Measurement Units (IMUs) are increasingly used for human motion analysis. However, two major challenges remain: First, one must know precisely in which orientation the sensor is attached to the respective body segment. This is commonly achieved by accurate manual placement of the sensors or by letting the subject perform tedious calibration movements. Second, standard methods for inertial motion analysis rely on a homogeneous magnetic field, which is rarely found in indoor environments. To address both challenges, we introduce an automatic calibration method for joints with two degrees of freedom such as the combined radioulnar and elbow joint. While the user performs arbitrary movements, the method automatically identifies the sensor-to-segment orientations by exploiting the kinematic constraints of the joint. Simultaneously, the method identifies and compensates the influence of magnetic disturbances on the sensor orientation quaternions and the joint angles. In experimental trials, we obtain angles that agree well with reference values from optical motion capture. We conclude that the proposed method overcomes mounting and calibration restrictions and improves measurement accuracy in indoor environments. It therefore improves the practical usability of IMUs for many medical applications.

Estimation of Gait Mechanics Based on Simulated and Measured IMU Data Using an Artificial Neural Network

“…Therefore, the use of machine learning algorithms or the exploitation of kinematic constraints seems to be most promising. The most recent advancements of the kinematic-constraint-based approaches (Laidig et al, 2017(Laidig et al, , 2019Müller et al, 2017;Nowka et al, 2019) have not been evaluated on gait analysis. Seel et al (2014) evaluated the knee and ankle joint sagittal plane angle achieving deviations to the gold standard of less then 1 • .…”

Lower Limb Kinematics Using Inertial Sensors during Locomotion: Accuracy and Reproducibility of Joint Angle Calculations with Different Sensor-to-Segment Calibrations

“…Since the movements are generally human-controlled, the accuracy of the axes definition essentially relies on the subjects' ability to precisely hold a given posture and on the execution of a given movement [9]. The third approach consists of exploiting the kinematic constraints of the joints and use almost arbitrary movements to perform the sensor-to-segment calibration [9,15]. This method is particularly adapted to single axis joints that can be satisfactorily modelled as a hinge joint like the knee; however, the modeling of spherical joints requires the execution of movements mostly around one axis to identify the joint axes [9], which resemble the functional method.…”

A Comparative Study of Markerless Systems Based on Color-Depth Cameras, Polymer Optical Fiber Curvature Sensors, and Inertial Measurement Units: Towards Increasing the Accuracy in Joint Angle Estimation

“…In that way, B 1 q B 2 (t) is then decomposed into 'ZXY' Euler angles. The rotation about z-axis represents the elbow flexion-extension angle, α, consistent with the ISB recommendations [16,36].…”