Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion

Abstract

Motion tracking based on commercial inertial measurements units (IMUs) has been widely studied in the latter years as it is a cost-effective enabling technology for those applications in which motion tracking based on optical technologies is unsuitable. This measurement method has a high impact in human performance assessment and human-robot interaction. IMU motion tracking systems are indeed self-contained and wearable, allowing for long-lasting tracking of the user motion in situated environments. After a survey on IMU-based human tracking, five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion. IMU based estimation was matched against motion tracking based on the Vicon marker-based motion tracking system considered as ground truth. Results show that all but one of the selected models perform similarly (about 35 mm average position estimation error).

Keywords: inertial measurements units; kinematics; motion tracking; sensor fusion.

Figure 1

Diagram of method 1 for the i-th limb, the diagram represents one temporal slice of the motion reconstruction. Vectors ${\mathbf{g}}_r$ and ${\mathbf{m}}_r$ are the gravity and the magnetic field vectors represented in the i-th limb frame in a reference configuration, e.g., N pose.

Figure 2

Diagram of method 2 for the i-th limb, the diagram represents one temporal slice of the motion reconstruction. Vectors ${\mathbf{g}}_r$ and ${\mathbf{m}}_r$ are the gravity and the magnetic field vectors represented in the i-th limb frame in a reference configuration, e.g., N pose.

Figure 3

Diagram of method 3 for the i-th limb, the diagram represents one temporal slice of the motion reconstruction. Dashed lines apply to the perfect version only whereas dotted lines to the pure version only. Vectors ${\mathbf{g}}_r$ and ${\mathbf{m}}_r$ are the gravity and the magnetic field vectors represented in the i-th limb frame in a reference configuration, e.g., N pose.

Figure 4

Diagram of the method 4, the diagram represents one temporal slice of the motion reconstruction.

Figure 5

Experimental setup showing markers that were placed on the anatomical landmarks and on the 9 mIMUs.

Figure 6

Alignment procedure for the mIMU-based data. mIMU-based estimated (Y), translated ($\tilde{Y}$) and aligned (Z) are reported along with OMC data (Z).

Figure 7

Error on the wrist motion reconstruction for the E${}_{\mathrm{FE}}$ functional motion trial. On the x-axis the seconds elapsed since the trial beginning are shown. Dotted lines represent average error in the trial.

Figure 8

Error on the wrist motion reconstruction for each period of the E${}_{\mathrm{FE}}$ functional motion trial. The boxplots show the median of E along with the 25th and 75th percentiles. Whiskers extend to 1.5 times over the interquartile range.