A Vision-Based Low-Cost Power Wheelchair Assistive Driving System for Smartphones
Bibliographic record
Abstract
Power wheelchairs (PWC) are essential for people with mobility impairment, and many research studies have been reported to ease their operations. However, the existing approaches either rely on extra hardware components or demand complex software that incurs high costs. In this work, we propose a low-cost, computer-vision-based assistive driving system that runs on a smartphone with the objective of safely driving a PWC in a hands-free manner with reduced attention in an indoor environment to relieve the arduous operations of disabled users and reduce their stress. The system adopts a modified and pre-trained ResNet-50 model running on a smartphone to derive the driving instructions using the images captured in real-time with its built-in camera. The smartphone interacts with a control interface to send the driving instructions to the PWC. A prototype of the proposed driving assistive system is implemented on a Pixel-6 Android phone and evaluated on a mobile robot as the proof-of-concept design. The experiments show that the smartphone can process input at up to 3.4 images per second to generate driving instructions in time to safely navigate the mobile robot at reasonable speeds in the testing environment with minimal intervention from the user.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".