Inertial measurement unit-based pose estimation: Analyzing and reducing sensitivity to sensor placement and body measures

Abstract

Introduction: Inertial measurement units have been proposed for automated pose estimation and exercise monitoring in clinical settings. However, many existing methods assume an extensive calibration procedure, which may not be realizable in clinical practice. In this study, an inertial measurement unit-based pose estimation method using extended Kalman filter and kinematic chain modeling is adapted for lower body pose estimation during clinical mobility tests such as the single leg squat, and the sensitivity to parameter calibration is investigated.

Methods: The sensitivity of pose estimation accuracy to each of the kinematic model and sensor placement parameters was analyzed. Sensitivity analysis results suggested that accurate extraction of inertial measurement unit orientation on the body is a key factor in improving the accuracy. Hence, a simple calibration protocol was proposed to reach a better approximation for inertial measurement unit orientation.

Results: After applying the protocol, the ankle, knee, and hip joint angle errors improved to $4.2^{\circ },6.3^{\circ }$ , and $8.3^{\circ }$ , without the need for any other calibration.

Conclusions: Only a small subset of kinematic and sensor parameters contribute significantly to pose estimation accuracy when using body worn inertial sensors. A simple calibration procedure identifying the inertial measurement unit orientation on the body can provide good pose estimation performance.

Keywords: Inertial measurement unit; calibration; clinical application; extended Kalman filter; forward kinematics; human pose estimation; joint angle; misorientation; sensitivity analysis.

Figure 1.

Seven DoF kinematic model of the right leg showing sensor positions, frame assignments, and displacement vectors. ${\mathbf{P}}_{\mathit{ankle}},{\mathbf{P}}_{\mathit{knee}},$ and ${\mathbf{P}}_{\mathit{hip}}$ refer to joint center position vectors and ${\mathbf{P}}_{\mathit{tibia}},{\mathbf{P}}_{\mathit{thigh}},$ and ${\mathbf{P}}_{\mathit{back}}$ refer to IMU position vectors.

Figure 2.

The ${\mathbf{r}}_{\mathbf{S}7}$ vector can be estimated as the summation of vectors $\mathbf{V}1$ and $\mathbf{V}2$, where $\mathbf{V}2$ is estimable using PD, PW and LL. $\mathbf{V}1$ is assumed to have only Y component equal to PD.

Figure 3.

Required parameters for pose estimation including PW, PD, LL, L_tibia, L_thigh, C_Tib, C_Thi, $L_{\mathit{Tib}2\mathit{Knee}},L_{\mathit{Thi}2\mathit{Knee}}$. Red cross signs correspond to the anatomical locations of the ankle, knee, and hip centers within the body. The back sensor is also made visible in the front view to show that it is placed at the same height as ASIS and PSIS bony landmarks above the hip center.

Figure 4.

Sensor and marker placement for the single leg squat experiment in the Motion Capture Lab.

Figure 5.

Different steps of performing the calibration protocol. In the left picture, the red arrow on the floor emphasizes the black guide line which is to make sure participants are standing in a correct frontal orientation. The red arrow on the table emphasizes that the sensors’ initial orientation (along the direction of the arrow) is to be parallel to participant’s sagittal plane. The two arrows are orthogonal.

Figure 6.

Pose estimation algorithm overview. ${\hspace{0.17em}}^d{R}_b$ is the rotation matrix from the body orientation to the desired orientation on the kinematic chain. ω and $\stackrel{\sim }{\omega }$ are measured and estimated values for angular velocity. $\stackrel{\mathrm{··}}{x}$ and $\stackrel{\stackrel{\sim }{\mathrm{··}}}{x}$ are measured and estimated values for linear acceleration. q and $\stackrel{\sim }{q}$ correspond to marker-based and estimated values for joint angle. $\stackrel{\stackrel{\sim }{·}}{q}$ and $\stackrel{\stackrel{\sim }{\mathrm{··}}}{q}$ are estimated values for joint velocity and acceleration.