Assessing Ankle Range of Motion with Wearable Technology: A Comparative Accuracy and Reliability Study
Bibliographic record
Abstract
Abstract Background Accurate measurement of ankle range of motion (ROM) is essential for diagnosing and treating musculoskeletal conditions, optimizing athletic performance, and managing neurological disorders such as Parkinson’s disease. This study evaluates the novel Ambulosono device, a sensor-based tool, against the traditional goniometer for assessing Ankle ROM in healthy participants. Methods A comparative cross-sectional study was conducted on 54 healthy participants aged 15 to 24 years. Ankle ROM was measured using the goniometer, placed on the lateral malleolus, and the Ambulosono device, secured on the dorsum of the foot. Participants performed maximal dorsiflexion and plantarflexion with knees extended, and five measurements were taken per device on both feet in randomized order. Statistical analyses included descriptive statistics, Bland-Altman plots, and Intraclass Correlation Coefficients (ICCs) to assess agreement and reliability. Results Mean goniometer ROM was 58.44° (SD=5.54) versus Ambulosono’s 56.80° (SD=3.88). No significant differences emerged between devices, foot sides, or gender. Bland-Altman analysis indicated agreement without P proportional bias. Reliability was excellent (Cronbach’s alpha=0.983, ICC=0.983). Conclusion The Ambulosono device is a robust tool that offers a reliable alternative to traditional goniometry, with advantages such as real-time feedback and reduced inter-rater variability. Its potential applications extend beyond clinical and athletic settings to include neurological rehabilitation and remote patient monitoring. Further research is warranted to validate its efficacy across diverse populations and real-world scenarios.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".