Evaluating surface EMG control of motorized wheelchairs for amyotrophic lateral sclerosis patients
Bibliographic record
Abstract
BACKGROUND: This study evaluated a novel control method for patients unable to independently control powered wheelchairs. Patients with amyotrophic lateral sclerosis often require a wheelchair but struggle with sufficient hand dexterity required for joystick control making them a population that needs this type of control method. METHODS: The study employed a novel control mechanism, using electromyography surface sensors applied to temporalis muscles able to measure the myoelectric voltage. Pattern and magnitude control of muscle contraction allowed for steering intention recognition and were used to manipulate their power wheelchair joystick. Four patients ages 51 to 69, two female and two male with amyotrophic lateral sclerosis, conducted Wheelchair Skills Test developed by Dalhousie University and were surveyed on the experience's Clinical Global Impression of Change. RESULTS: Findings showed independent steering was capable for patients without hand function and provided recommendations for improved human-machine interface. All patients demonstrated the ability to engage the system, with varying precision, for driving their wheelchair in a controlled environment. CONCLUSIONS: Three patients in the pilot trial reported the highest score of clinical global impression of change, all of whom had lost independent control of their wheelchair joystick. Patient four retained impaired hand dexterity for joystick control and reported negative impression of change, comparatively. Feedback from the study will be leveraged to improve training outcomes. Trial registration Subjects provided signed informed consent according to the Declaration of Helsinki to enter the study that was approved by the Mayo Clinic Institutional Review Board in Rochester, Minnesota. The study is registered on ClinicalTrials.gov under identifier NCT04800926 as of March 14, 2021 retrospectively registered.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".