Investigation of EMG Parameters for Transtibial Amputees While Treadmill Walking with Different Speeds: A Preliminary Study
Bibliographic record
Abstract
Electromyography (EMG) is the process of acquiring electrical signals generated through muscle activity (contraction/relaxation). Surface EMG deliberates the amount of electrical activity in the musculoskeletal system in a non-invasive way. Under specific conditions and during certain motor activities, this signal is substantially associated with muscle strength. These Signals are used as Control Inputs by assistive devices. The study aimed to investigate the EMG parameters of lower limb muscles (rectus femoris and biceps femoris) in healthy individuals and transtibial amputees walking on a treadmill at different speeds (0.55 m/s, 0.83 m/s, and 1.11 m/s). Ten non-amputee and two amputee subjects participated. Findings reveal significant reductions in EMG signals at slower speeds, emphasizing foot stability. The right biceps femoris exhibits the highest signals average, while the right rectus femoris has the lowest for amputees. The male participants’ right biceps femoris muscle showed the greatest signals of average treadmill walking activity at 0,55 m/s (0.0014 V) compared to the amputee individuals’ (0.001 V). At (0,83 m/s), male participants (0.0015 V) outperformed amputee subjects (0.0004 V). At (1,11 m/s), male participants (0.0024 V) outperformed amputee subjects (0.001 V). Male participants consistently outperform amputees across speeds. The study suggests the potential application of findings in rehabilitating transtibial amputees on a treadmill, considering distance and maximum speed with a prosthesis. Overall, slow walking pace impacts EMG signals, providing insights for clinicians developing interventions for amputee rehabilitation.
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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 | 0.000 |
| 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) | 0.002 | 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".