Semi-Automatic Sensor-to-Body Calibration of Inertial Sensors on Lower Limb Using Gait Recording
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Bibliographic record
Abstract
An inertial measurement unit (IMU) is the ideal technology for ambulatory measurement of human motion. However, because an IMU measures acceleration and angular velocity in its sensor frame, to obtain clinically meaningful kinematics, a calibration procedure is required to align the IMU frame with the anatomical frame of its corresponding segment. This paper aims to investigate whether recording of straight walking could be used for sensor-to-body calibration of IMUs instead of performing calibration-specific movements. For this purpose, after three to five seconds of quiet standing, ten participants walked for eight steps. To obtain the sensor-to-body transformations, motions of the thigh, shank, and foot segments were recorded by three IMUs. The accuracy and repeatability of the transformations obtained by the IMUs were compared to the reference anatomical frames obtained by the motion capture system. Statistical analysis showed no significant difference (p>0.05) between the calibration outcome in Test and Retest sessions. The accuracy and inter-participant repeatability of straight walking (coefficient of variation: 20.5% to 53.5%) were comparable to those of more sophisticated calibration procedures reported in the literature (coefficient of variation: 18.1% to 50.1%). The proposed calibration reduced the offset errors (e.g., from 26.3° for knee internal/external rotation without calibration to 17.1°) and RMSE of 3D joint angle estimation during over-ground walking. It also made the range of motion estimation significantly more repeatable (p<; 0.05). Therefore, using IMUs, we can measure clinically meaningful lower limb joint angles when we use straight walking data for the sensor-to-body calibration.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it