Evaluation of an ambient noise insensitive hum-based powered wheelchair controller
Bibliographic record
Abstract
PURPOSE: A recently-developed assistive technology nicknamed "the Hummer" was investigated as a potential powered wheelchair controller for individuals with severe and multiple disabilities. System performance in a noisy environment was compared to that obtained with a commercial automatic speech recognition (ASR) system. METHOD: A bi-hum driving protocol was developed to allow the Hummer to serve as a powered wheelchair controller. Participants performed several virtual wheelchair driving tasks of increasing difficulty using the two systems. Custom-written software recorded task execution time, number of commands issued and wall collisions, speed, and trajectory. RESULTS: The bi-hum protocol was shown to be non-intuitive and required user training. Overall, the Hummer achieved lower performance relative to ASR. Once users became accustomed to the protocol, the difference in performance between the two systems became insignificant, particularly for the higher-difficulty task. CONCLUSIONS: The Hummer provides a promising new alternative for powered wheelchair control in everyday environments for individuals with severe and multiple disabilities who are able to hum, particularly for those with severe dysarthria which precludes ASR usage. A more intuitive driving protocol is still needed to reduce user frustration and mitigate user-generated errors; recommendations on how this can be achieved are given herein. [Box: see text].
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".