An easy-to-use wearable step counting device for slow walking using ankle force myography
Bibliographic record
Abstract
Step counting is a practical way for evaluating the activity level of people in daily life. However, the widely used accelerometer-based step-counters are not able to accurately detect low-speed steps (<;0.6 m/s). Our earlier study used supervised machine learning to achieved a very high performance (error rate <;1.5%) in low speed step detection based on the force myography (FMG) signals recorded at the ankle. However, the supervised machine learning approach requires a training process using carefully labelled data. The present study explores the feasibility of using unsupervised learning technique to improve the usability of the ankle force sensing resisters (FSR) band in step detection. An unsupervised K-Means algorithm was employed to train and test with the FMG data recorded from an array of 8 FSRs worn on the ankle position. Eight young healthy volunteers participated in the study by walking on a treadmill at 3 different speeds (0.28 m/s, 0.42 m/s, and 0.56 m/s) while FMG signals were recorded. Results showed a low error rate in the step detection (2.2%) at all 3 walking speeds using the unlabelled data for training.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".