Modelling and Simulation of an Ultrasonic Tethering Smart Wheelchair System for Social Following
Bibliographic record
Abstract
Distracted navigation causes 20% of all powered wheelchair accidents. In social situations, wheelchair users must divide their attention between navigating the chair and conversing with an accompanying person. These conversations could lead to increased mental stress and distractions from maneuvering the chair. This project aims to eliminate the need to manually control a powered wheelchair when moving and conversing with an accompanying person, by controlling the wheelchair’s path to follow beside a person. This includes identifying and determining the person’s pose to control wheelchair navigation. The proposed ultrasonic tethering system was developed and simulated on Matlab and Simulink using models for ultrasonic sensors, amplification and filtering circuits, and a processing unit. Unlike infra-red sensors and cameras that are highly dependent on environmental light conditions, ultrasonic sensors are inexpensive and independent of environmental conditions. Simulation results determined wheelchair direction based on the accompanying person’s position, suggesting that ultrasonic tethering can be used for side-by-side following. The simulation results can be used to determine circuit component parameters for developing an ultrasonic tethering prototype.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".