Effects of Electrode Position Targeting in Noninvasive Electromyography Technologies for Finger and Hand Movement Prediction
Bibliographic record
Abstract
Abstract Purpose Stroke patients may need to undergo rehabilitation therapy to improve their mobility. Electromyography (EMG) can be used to improve the effectiveness of at-home therapy programs, as it can assess recovery progress in the absence of a health professional. In particular, EMG armbands have the advantage of being easy to use compared to other EMG technologies, which could allow patients to complete therapy programs without external assistance. However, it is unclear whether there are drawbacks associated with the fixed electrode placement imposed by current armband designs. This study compared the hand gesture prediction capabilities of an off-the-shelf EMG armband with fixed electrode placement and an EMG setup with flexible electrode positioning. Methods Ten able-bodied participants performed a series of hand and finger gestures with their dominant hand, once with an EMG armband (Untargeted condition) and once with electrodes deliberately placed on specific muscles (Targeted condition). EMG features were extracted from overlapping sliding windows and were used to (1) classify the gestures and (2) predict finger joint positions as measured by a robotic hand exoskeleton. Results For the classification task, a logistic regression model performed significantly better ( $$p < 0.001$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo><</mml:mo> <mml:mn>0.001</mml:mn> </mml:mrow> </mml:math> ) for the Targeted condition ( $$55.8\% \pm 10.1\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>55.8</mml:mn> <mml:mo>%</mml:mo> <mml:mo>±</mml:mo> <mml:mn>10.1</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> ) compared to the Untargeted condition ( $$47.9\% \pm 11.6\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>47.9</mml:mn> <mml:mo>%</mml:mo> <mml:mo>±</mml:mo> <mml:mn>11.6</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> ). For the regression task, a k -nearest neighbours model obtained significantly lower ( $$p = 0.007$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.007</mml:mn> </mml:mrow> </mml:math> ) mean RMSE values for the Targeted condition ( $$0.260 \pm 0.037$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>0.260</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.037</mml:mn> </mml:mrow> </mml:math> ) compared to the Untargeted condition ( $$0.270 \pm 0.043$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>0.270</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.043</mml:mn> </mml:mrow> </mml:math> ). Conclusion We observed a trade-off between predictive accuracy and ease-of-use of the EMG devices used in this study. It is important to consider such a trade-off when developing clinical applications such as at-home stroke rehabilitation therapy programs.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".