Gait event detection for FES using accelerometers and supervised machine learning
Bibliographic record
Abstract
Rule based detectors were used with a single cluster of accelerometers attached to the shank for the real time detection of the main phases of normal gait during walking. The gait phase detectors were synthesized from two rule induction algorithms, Rough Sets (RS) and Adaptive Logic Networks (ALNs), and compared with to a previously reported stance/swing detector based on a hand crafted, rule based algorithm. Data was sampled at 100 Hz and the detection errors determined at each sample for 50 steps. For three able bodied subjects, the sample by sample accuracy of stance/swing detection ranged within 94-97%, 87-94%, and 87-95% for the RS, ALN, and the handcrafted methods, respectively. A heuristically formulated postdetector filter improved the RS and ALN detectors' accuracy to 98%. RS and ALN also detected five gait phases to an overall accuracy of 82-89% and 86-91%, respectively. The postdetector filter localized the errors to the phase transitions, but did not change the detection accuracy. The average duration of the error at each transition was 40 ms and 23 ms for RS and ALN, respectively. When implemented on a microcontroller, the RS-based detector executed ten times faster and required one tenth of the memory than the ALN-based detector.
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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 | 0.001 | 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".