Control an exoskeleton for forearm rotation using FMG
Bibliographic record
Abstract
In the field of robotic rehabilitation, surface electromyography (sEMG) has been proposed for controlling exoskeleton device for assisting different movements of the human joints, such as the shoulder, the elbow, the wrist and the fingers. However, few works have been proposed for using sEMG to control a forearm exoskeleton for assisting the movement of pronation and supination. The main difficulty for employing the sEMG control approach is the low signal to noise ratio of the pronator and supinator muscle group. To overcome this difficulty, we propose an alternative method utilizing force myography (FMG) instead of the sEMG for controlling a forearm pronation/supination exoskeleton. An easy setup strap with an array of force sensors was developed to capture the forearm FMG signal. The FMG signal was processed and classified using the state-of-art machine learning algorithm - Extreme Learning Machine (ELM) to predict the forearm position. The prediction results can be used to control a forearm pronation/supination exoskeleton. A bilateral experiment with two protocols was designed to demonstrate one of the potential applications of the proposed system, as well as to evaluate the system performance in terms of classification accuracy. One volunteer participated in the experiment. The result shows the system was able to predict the position of the forearm using the proposed method with 98.36% and 96.19% of accuracy.
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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".