Movement Outcomes Acquired via Markerless Motion Capture Systems Compared with Marker-Based Systems for Adult Patient Populations: A Scoping Review
Bibliographic record
Abstract
Mobile motion capture is a promising technology for assessing physical movement; markerless motion capture systems (MLSs) offer great potential in rehabilitation settings, given their accessibility compared to marker-based motion capture systems (MBSs). This review explores the current literature on rehabilitation, for direct comparison of movement-related outcomes captured by MLSs to MBSs and for application of MLSs in movement measurements. Following a scoping review methodology, nine databases were searched (May to August 2023). Eligible articles had to present at least one estimate of the mean difference between a measure of a physical movement assessed by MLS and by MBS. Sixteen studies met the selection criteria and were included. For comparison of MLSs with MBSs, measures of mean joint range of motion (ROM) displacement were found to be similar, while peak joint angle outcomes were significantly different. Upper body movement outcomes were found to be comparable, while lower body movement outcomes were very different. Overall, nearly two-thirds of measurements identified statistical differences between MLS and MBS outcomes. Regarding application, no studies assessed the technology with patient populations. Further MLS-specific research with consideration of patient populations (e.g., intentional error testing, testing in less-than-ideal settings) would be beneficial for utilization of motion capture in rehabilitation contexts.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".