Validation of functional calibration and strap-down joint drift correction for computing 3D joint angles of knee, hip, and trunk in alpine skiing

Abstract

To obtain valid 3D joint angles with inertial sensors careful sensor-to-segment calibration (i.e. functional or anatomical calibration) is required and measured angular velocity at each sensor needs to be integrated to obtain segment and joint orientation (i.e. joint angles). Existing functional and anatomical calibration procedures were optimized for gait analysis and calibration movements were impractical to perform in outdoor settings. Thus, the aims of this study were 1) to propose and validate a set of calibration movements that were optimized for alpine skiing and could be performed outdoors and 2) to validate the 3D joint angles of the knee, hip, and trunk during alpine skiing. The proposed functional calibration movements consisted of squats, trunk rotations, hip ad/abductions, and upright standing. The joint drift correction previously proposed for alpine ski racing was improved by adding a second step to reduce separately azimuth drift. The system was validated indoors on a skiing carpet at the maximum belt speed of 21 km/h and for measurement durations of 120 seconds. Calibration repeatability was on average <2.7° (i.e. 3D joint angles changed on average <2.7° for two repeated sets of calibration movements) and all movements could be executed wearing ski-boots. Joint angle precision was <4.9° for all angles and accuracy ranged from -10.7° to 4.2° where the presence of an athlete-specific bias was observed especially for the flexion angle. The improved joint drift correction reduced azimuth drift from over 25° to less than 5°. In conclusion, the system was valid for measuring 3D joint angles during alpine skiing and could be used outdoors. Errors were similar to the values reported in other studies for gait. The system may be well suited for within-athlete analysis but care should be taken for between-athlete analysis because of a possible athlete-specific joint angle bias.

Embedded inertial measurement unit reveals pole lean angle for cross-country skiing

“…While IMUs have been deployed to study skiing technique [18][19][20][21], they have not been used to study the motion of the pole beyond detecting pole-terrain contact events, cycle duration, cycle speed, and cycle length [19,22]. Nevertheless, a pole-embedded IMU also provides the requisite data (acceleration and angular velocity) needed to estimate the lean angle of the pole as well.…”

Magnetic Condition-Independent 3D Joint Angle Estimation Using Inertial Sensors and Kinematic Constraints

“…The approaches proposed in [11] and [18] are of our particular interest. In [18], Fasel et al proposed a joint drift correction method for computing 3D joint angles of the knee, hip, and trunk for highly dynamic movements, e.g., in alpine skiing.…”

“…Then, based on the two mapped acceleration vectors with respect to the global frame, the joint drift in the form of a quaternion was determined to match the two vectors. This correction method in [18] was improved in [11] by adding a second step to separately reduce the heading drift. Note that, while the constraints used in [11] and [39] are the same, the purposes of using the constraints was different, i.e., [39] to determine the constant segment-fixed vectors and [11] to estimate the drift.…”

“…reference, forming an inertial and magnetic measurement unit (IMMU). Last two decades, much progress has been made in joint angle estimation using IMUs or IMMUs [1,2,5,[7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25].Seel et al[7] estimated the flexion/extension angles of the knee and ankle during walking based on IMUs because the flexion/extension is the rotation around one dominant axis of the joint. El-Gohary and McNames [8] presented an unscented Kalman filter (UKF) incorporated with a kinematic arm model to track the shoulder and elbow joint angles using IMUs.…”

“…While the proposed method estimates a 3D joint angle, it does not use a magnetometer and, instead, uses a kinematic constraint. Accordingly, it is natural that the proposed method is completely free from the magnetic disturbance issue.The use of constraints has been proposed in previous studies [2,7,11,12,[16][17][18][35][36][37][38]. Hu and Sun [35] proposed a state-constrained Kalman filter (KF) for compensation of the hard iron effect for the magnetometer based on prior knowledge, e.g., the norm of the gravity vector is 9.8 m/s 2 .…”