Assessment of Pelvic Motion During Single-Leg Weight-Bearing Tasks Using Smartphone Sensors: Validity Study
Bibliographic record
Abstract
Background: Clinicians and athletic training specialists often assess the performance of single-leg, weight-bearing tasks to monitor rehabilitation progress and guide exercise progression. Some of the key metrics assessed are excessive pelvic motion, balance, and duration of each repetition of the exercise. Motion can be objectively characterized using motion capture (MOCAP); however, MOCAP is often not available in clinics due to the high costs and complexity of the analyses. Smartphones have built-in sensors that can be used to measure changes in body segment orientation and acceleration, which may make them a more feasible and affordable technology to use in practice. Objective: This study aimed to determine if, compared to gold-standard MOCAP, smartphone sensors can provide valid measures of pelvic orientation, acceleration, and repetition duration during single-leg tasks in healthy individuals. Methods: Overall, 52 healthy participants performed single-leg squats and step-down tasks from heights of 15 and 20 cm. Pelvic motion was assessed using MOCAP and a smartphone placed over the sacrum. The MATLAB (MathWorks) mobile app was used to collect smartphone acceleration and orientation data. Individual repetitions of each exercise were manually identified, and the following outcomes were extracted: duration of the repetition, mediolateral acceleration, and 3D pelvic orientation at peak squat. Validity was assessed by comparing metrics assessed with a smartphone and MOCAP using intraclass correlation coefficients (ICCs) and paired Wilcoxon tests. Differences between tasks were compared using 1-way ANOVA or the Friedman test. Results: Across the 3 single-leg tasks, smartphone estimates demonstrated consistently high agreement with the MOCAP for all metrics (ICC point estimates: >0.8 for mediolateral acceleration and frontal plane orientation; >0.9 for squat duration and orientation on the sagittal and transverse plane). Bias was identified for most outcomes (multiple P<.001). Both smartphone and MOCAP recordings identified clear differences between tasks, with step-down tasks usually requiring larger changes in pelvic orientation and larger mediolateral sways. Duration did not differ between tasks. Conclusions: Despite a consistent bias, the smartphone demonstrated good to excellent validity relative to gold-standard MOCAP for most outcomes. This demonstrates that smartphones offer an accessible and affordable tool to objectively characterize pelvic motion during different single-leg weight-bearing tasks in healthy participants. Together with earlier reports of good between-day reliability of similar measures during single-leg squats, our results suggest that smartphone sensors can be used to assess and monitor single-leg task performance. Future studies should investigate whether smartphone sensors can aid in the assessment and treatment of people with musculoskeletal disorders. More user-friendly interfaces and data analysis procedures may also facilitate the implementation of this technology in practice.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".