Concurrent validity of inertial measurement units in range of motion measurements of upper extremity: A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Inertial measurement units (IMUs) have proven to be valuable tools in measuring the range of motion (RoM) of human upper limb joints. Although several studies have reported on the validity of IMUs compared to the gold standard (optical motion capture system, OMC), a quantitative summary of the accuracy of IMUs in measuring RoM of upper limb joints is still lacking. Thus, the primary objective of this systematic review and meta-analysis was to determine the concurrent validity of IMUs for measuring RoM of the upper extremity in adults. Fifty-one articles were included in the systematic review, and data from 16 were pooled for meta-analysis. Concurrent validity is excellent for shoulder flexion–extension (Pearson’s r = 0.969 [0.935, 0.986], ICC = 0.935 [0.749, 0.984], mean difference = −3.19 ( p = 0.55)), elbow flexion–extension (Pearson’s r = 0.954 [0.929, 0.970], ICC = 0.929 [0.814, 0.974], mean difference = 10.61 ( p = 0.36)), wrist flexion–extension (Pearson’s r = 0.974 [0.945, 0.988], mean difference = −4.20 ( p = 0.58)), good to excellent for shoulder abduction–adduction (Pearson’s r = 0.919 [0.848, 0.957], ICC = 0.840 [0.430, 0.963], mean difference = −7.10 ( p = 0.50)), and elbow pronation–supination (Pearson’s r = 0.966 [0.939, 0.981], ICC = 0.821 [0.696, 0.900]). There are some inconsistent results for shoulder internal–external rotation (Pearson’s r = 0.939 [0.894, 0.965], mean difference = −9.13 ( p < 0.0001)). In conclusion, the results support IMU as a viable instrument for measuring RoM of upper extremity, but for some specific joint movements, such as shoulder rotation and wrist ulnar-radial deviation, IMU measurements need to be used with caution.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".