Subtask Segmentation of the L Test Using Smartphone Inertial Measurement Units
Bibliographic record
Abstract
The L Test of Functional Mobility is used in rehabilitation to assess an individual’s mobility status and dynamic balance. Segmenting subtasks of functional mobility tests can allow clinicians to further identify problematic movements and fall risk for an individual. This research evaluated a rule-based method for L Test subtask segmentation. Twenty able-bodied participants completed five L test trials with a smartphone attached to a belt at their posterior pelvis. A custom-designed walk test app collected accelerometer, gyroscope, and magnetometer data. Smartphone video recordings of each trial were used to determine subtask timing ground truth. A novel segmentation algorithm was developed and attained 97.9% accuracy, 98.5% specificity, and 86.1% sensitivity for stand-up; 94.6% accuracy, 96.2% specificity, and 72.9% sensitivity for sit-down; and 90.8% accuracy, 96.4% sensitivity, and 70.9% specificity for all turns. These experimental results show that the algorithm has potential for use in subtask segmentation of the L Test and should be further assessed on individuals with mobility disabilities.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".