A Technique to Detect Fatigue in the Lower Limbs
Bibliographic record
Abstract
As muscles fatigue, their passive and active mechanical properties change increasing the susceptibility of the human body to damage. The state-of-the-art technique for muscle fatigue detection, EMG signals, is cumbersome. This paper presents a technique to detect fatigue by tracking a kinematic parameter of the musculoskeletal system. The method uses the time-history of a single joint angle to detect fatigue in the lower limbs. A sensor is mounted to the knee joint to measure the knee flexion angle. Time delay embedding is used to track the orbit of knee joint motions in a reconstructed phase-space. The reconstructed phase-space allows us to obtain information about other body parts and joints of the lower limb in addition to the knee joint, since they are all connected in an open kinematic chain. Long-time drift in the orbit location and shape in phase-space is quantified and used as a measure of lower limb fatigue. The proposed technique presents a mobile, wireless, and cheap method to assess fatigue that can act as an early warning system for the lower limb.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".