External Sensors for Detecting the Activation and Deactivation Times of the Major Muscles Used in Walking
Bibliographic record
Abstract
Functional electrical stimulation (FES) can improve walking in individuals with mobility impairments. We evaluated accelerometers, force sensitive resistors, segment angles, and segment angular velocities to identify which sensor best determines the activation and deactivation times of the main muscles used during walking. This sensor(s) can be used in the future in conjunction with FES systems to improve walking. Able-bodied subjects walked at various speeds. Threshold levels were set for each sensor that minimized the difference between the times of activating and deactivating the electromyogram (EMG) of six muscles and the times of sensor threshold crossings as a percent of the step cycle. Mobility-impaired subjects walked at their preferred speed with and without FES to correct foot drop. Thresholds were set for these subjects so that sensor signals would cross at times that matched those of able-bodied subjects. Segment angles were generally the most effective sensor signals. Using segment angles of the thigh, shank, and foot, activation and deactivation times of the six muscles could be determined to within 6% of the step cycle. The shank segment angle produced the lowest overall error and was among the top three sensors for 10 of the 12 events (activation and deactivation of six muscle groups). A segment angle sensor was implemented using a complementary filter (accelerometer/gyroscope combination). Using this sensor improved rule-based timing of FES in subjects with foot drop as compared to accelerometers alone.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".