Optimization of IMU Sensor Placement for the Measurement of Lower Limb Joint Kinematics

Abstract

There is an increased interest in using wearable inertial measurement units (IMUs) in clinical contexts for the diagnosis and rehabilitation of gait pathologies. Despite this interest, there is a lack of research regarding optimal sensor placement when measuring joint kinematics and few studies which examine functionally relevant motions other than straight level walking. The goal of this clinical measurement research study was to investigate how the location of IMU sensors on the lower body impact the accuracy of IMU-based hip, knee, and ankle angular kinematics. IMUs were placed on 11 different locations on the body to measure lower limb joint angles in seven participants performing the timed-up-and-go (TUG) test. Angles were determined using different combinations of IMUs and the TUG was segmented into different functional movements. Mean bias and root mean square error values were computed using generalized estimating equations comparing IMU-derived angles to a reference optical motion capture system. Bias and RMSE values vary with the sensor position. This effect is partially dependent on the functional movement analyzed and the joint angle measured. However, certain combinations of sensors produce lower bias and RMSE more often than others. The data presented here can inform clinicians and researchers of placement of IMUs on the body that will produce lower error when measuring joint kinematics for multiple functionally relevant motions. Optimization of IMU-based kinematic measurements is important because of increased interest in the use of IMUs to inform diagnose and rehabilitation in clinical settings and at home.

Evaluating the Impact of IMU Sensor Location and Walking Task on Accuracy of Gait Event Detection Algorithms

“…Each participant was prepared for the experiment by securely placing sensors at each location on the right leg (see Figure 1). Although other locations on the upper thigh and lower torso were identified as common locations for IMU sensors in previous work [23], Panebianco et al found that torso-based algorithms generally performed worse than shank-or footbased algorithms [2], so only shank and foot locations were selected for investigation in this study. Additionally, very few studies report on thigh-based algorithms.…”

“…A sensor-to-body calibration was used to transform IMU data into data in terms of the anterior-posterior (AP), medial-lateral (ML), and superior-inferior (SI) anatomical directions. The calibration approach used here has been previously described [23,41]. Briefly, this approach utilizes two static poses to determine the direction of anatomical axes using gravitational acceleration.…”

Validation of a portable marker-based motion analysis system

“…The kinematic differences between the Opti_knee gait system and biplanar fluoroscopy system were quantified based on root mean square error (RMSE) [ 20 ] using Microsoft Office Excel (2013 version, Microsoft, Redmond, WA, USA). Bivariate Pearson correlations were calculated to compare the similarity trends between the two techniques using SPSS version 22.0 (IBM Corp., Armonk, NY, USA).…”

Treadmill-to-Overground Mapping of Marker Trajectory for Treadmill-Based Continuous Gait Analysis

“…This is why gait analysis is constantly being actively studied. Marker-based optical motion capture (MoCap) systems are considered a gold standard in biomechanical gait research due to their high accuracy [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. In MoCap-based gait analysis, markers are attached to the lower limb of subjects and the gait is analyzed using the trajectories of these markers.…”