Estimation of 3D Knee Joint Angles during Cycling Using Inertial Sensors: Accuracy of a Novel Sensor-to-Segment Calibration Procedure Based on Pedaling Motion

Abstract

This paper presents a novel sensor-to-segment calibration procedure for inertial sensor-based knee joint kinematics analysis during cycling. This procedure was designed to be feasible in-field, autonomously, and without any external operator or device. It combines a static standing up posture and a pedaling task. The main goal of this study was to assess the accuracy of the new sensor-to-segment calibration method (denoted as the ‘cycling’ method) by calculating errors in terms of body-segment orientations and 3D knee joint angles using inertial measurement unit (IMU)-based and optoelectronic-based motion capture. To do so, 14 participants were evaluated during pedaling motion at a workload of 100 W, which enabled comparisons of the cycling method with conventional calibration methods commonly employed in gait analysis. The accuracy of the cycling method was comparable to that of other methods concerning the knee flexion/extension angle, and did not exceed 3.8°. However, the cycling method presented the smallest errors for knee internal/external rotation (6.65 ± 1.94°) and abduction/adduction (5.92 ± 2.85°). This study demonstrated that a calibration method based on the completion of a pedaling task combined with a standing posture significantly improved the accuracy of 3D knee joint angle measurement when applied to cycling analysis.

Mobile Applications in Evaluations of Knee Joint Kinematics: A Pilot Study

“…Analysing the literature, one can see a tendency that, for the knee joint, mainly measurements of the angle of flexion and extension are collected, which, according to the authors of these articles, are keyed in the monitoring knee function [47,49]. However, limiting yourself to only this one scope excludes the assessment of dynamic valgus described by Hewett et al [50], which was also observed during pedalling motion by Cordillet et al [51]. Moreover, the sensors used in our project also analysed the SSE parameter, interpreted as deviations in the frontal plane from the correct trajectory described by us in the methods section.…”

Gyro-based lower limb asymmetry during a 4-km time trial on a velodrome among high level female cyclists

“…Before the trial, an anatomical calibration procedure was performed in order to align IMU sensor frame to body segment frame following a method previously evaluated (Cordillet et al 2019). Such a method allows to obtain angular rates in relation to the body segment frames.…”

Using Accelerometer Data to Tune the Parameters of an Extended Kalman Filter for Optical Motion Capture: Preliminary Application to Gait Analysis

“…This Earth-fixed frame is usually defined as NED (North-East-Down) or NWU (North-West-Up), and will be probably rotated with respect to the reference frame used by the motion capture lab. Therefore, the first step is to determine the offset between both reference frames, for which two methods can be applied: (i) the first option is to carry out a preliminary IMU calibration process, as the spot check proposed in [ 11 ]; (ii) the second alternative is to attach three markers to each IMU, so the local frames can be obtained directly from the optical motion capture system [ 38 ]. Since the second method requires a large number of markers, thus making the motion capture process more involved and error-prone, the calibration approach has been chosen in this work.…”