Classification of Task Weight During Dynamic Motion Using EEG–EMG Fusion
Bibliographic record
Abstract
Musculoskeletal disorders are the biggest cause of disability worldwide and wearable mechatronic rehabilitation devices have been proposed as a potential tool for providing treatment; however, before such devices can be widely adopted, improvements in reliability are necessary. Changes in system dynamics caused by user actions, such as picking up a weight, can greatly affect control stability; hence, detecting these changes can lead to improved system performance. It is difficult to integrate conventional sensing technologies for completing this task into a wearable device in an unobtrusive way, therefore an alternative solution using bioelectrical signals, such as electroencephalography (EEG) and electromyography (EMG), to detect task weight is proposed. In this study, EEG and EMG signals were collected during dynamic elbow flexion-extension motion at different speeds, while holding different weights. These biosignals were used to develop different EEG-EMG fusion models to classify the weight the user was holding while moving. It was found that using a Weighted Average fusion method, and incorporating speed information into the model, provided the best performance, with an accuracy of 83.01 ± 6.04% when classifying three task weights. This work demonstrated the feasibility of using EEG-EMG fusion for classification of task weight during dynamic motion, which can be used to improve the adaptability and robustness of wearable mechatronic rehabilitation devices.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".