Voice-activated wheelchair: An affordable solution for individuals with physical disabilities
Bibliographic record
Abstract
The Low-Cost Voice Controlled Wheelchair with Raspberry Pi is an innovative assistive technology designed to improve the mobility and independence of people with disabilities. This research aims to develop a wheelchair system that can be operated using voice commands at an affordable price, making it accessible to a wider range of individuals with limited mobility. The device is built on the Raspberry Pi, a reasonably priced, credit-card-sized computer, and uses an easy-to-use yet efficient voice recognition technique to let users control the wheelchair with their vocal commands. A Raspberry Pi, a microphone, and motor controllers are some of the system's hardware components. The software uses Python programming language and open-source voice recognition technology to recognize voice commands, making it easy for users to navigate their environment independently. The system has been tested on a prototype and has shown promising results in terms of accuracy and reliability. The Low-Cost Voice Controlled Wheelchair with Raspberry Pi can give disabled persons new levels of mobility and independence, enhancing their quality of life and enhancing their capacity to carry out daily tasks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".