Activity classification in persons with stroke based on frequency features
Bibliographic record
Abstract
Recent advances in the use of inertial measurement units (IMUs) for motion analysis suggest the possibility of using this technology for the monitoring of daily activities of individuals during rehabilitation post-stroke. Previous studies have utilized features extracted from accelerometer and gyroscope signals to develop classification models capable of identifying activities performed within large datasets. In this study, nine k-nearest neighbor cross-validated classifiers were developed using frequency-features derived from shank-mounted IMUs on the less-affected and affected limbs of subjects with stroke. These classifiers were evaluated for two separate datasets of post-stroke gait; the first a classification of three separate gait activities (overground walking, stair ascent, and stair descent), and the second a classification of five gait activities, overground walking, stair ascent, and descent with a distinction between stepping pattern used while negotiating stairs (step-over-step (SOS) and step-by-step (SBS)). The comparison showed the highest classification accuracy, 100% for the three-activities and 94% for the five-activities, was obtained using a classifier composed of features derived from accelerometer and gyroscope measurements from both IMUs on less-affected and affected limbs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".