Towards in-field assessment of humeral and scapular kinematics: a comparison between laboratory and field settings using inertial sensors
Bibliographic record
Abstract
Introduction Inertial measurement units allow for quantitative assessment of body motion in many environments. Determining the ability to measure upper limb motion with inertial measurement units, leveraging procedures traditionally used in the lab such as scapular calibration procedures and humeral axial rotation calculation, would expand the opportunities to assess upper limb function in externally valid environments. This study examined if humeral and scapular motion measured in different field settings is consistent with motion measured in a lab setting in similar tasks. Methods Twenty-eight adults participated in the study (14 field setting, 14 lab setting). Three different types of field settings were included: home (n = 5), work (n = 4), and sports (n = 5). Field participants were matched to lab participants based on sex and body height. All participants were equipped with five inertial and magnetic measurement units (Xsens Awinda, Xsens Technlogies, NL, Fs = 100 Hz) on the torso, humeri, and scapulae. Humeral and scapular angles were measured during a functional task protocol consisting of seven tasks. Data from all three field settings were combined. Statistical parametric mapping (α = .05) was used to assess differences in waveforms between the lab and field data. Results and discussion Five out of seven tasks displayed no differences for humeral elevation and humeral axial rotation, while scapular upward rotation and tilt were not statistically different for any tasks. Scapular internal rotation variability was very high for the field setting, but not for the lab setting. Task-based differences in humeral elevation and humeral axial rotation may be related to equipment modifications for the field protocol and between subjects' variability in task performance. Data indicate that humeral elevation, humeral axial rotation, and scapular upward rotation can be measured in externally valid field settings, which is promising for the evaluation of upper limb movement in natural environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".