Identifying Interaction Forces Via EMG Under Changing Motion Dynamics
Bibliographic record
Abstract
Musculoskeletal injuries can severely inhibit performance of activities of daily living. In order to regain function, rehabilitation is often required. Assistive rehabilitation devices can be used to increase arm mobility by guiding therapeutic exercises or assisting with motion. Electromyography (EMG) may be able to provide an intuitive interface between the patient and the device if appropriate classification models allow smart systems to relate these signals to the desired device motion. Unfortunately, the accuracy of pattern recognition models classifying motion in constrained laboratory environments significantly drops when used for detecting dynamic unconstrained movements. The objectives of this study were to quantity how various motion factors affect arm muscle activations during dynamic motion, and to use these motion factors and EMG signals for detecting interaction forces between the person and the environment during motion. The results quantity how EMG features change significantly with variations in arm positions, interaction forces, and motion velocities. The results also show that pattern recognition models were able to detect intended characteristics of motion based solely on EMG signals. Prediction of force was improved from 73.77% correct to 79.17% accuracy during elbow flexion-extension by properly selecting the features, and providing measurable arm position and velocity information as additional inputs to a linear discriminant analysis model.
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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) | 0.001 | 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".