Advancements in intelligent wheelchairs: a scoping review
Bibliographic record
Abstract
Abstract When designed to meet users’ needs and consider their environment, wheelchairs have the capability to increase participation and positively affect the users’ quality of life. The application of artificial intelligence (AI) techniques has tremendous potential to enhance the intelligence aspect of powered wheelchairs (PW) by providing solutions to predict harms, collect a variety of data, including new and existing data, and contribute to comfort improvement initiatives. However, the use of existing intelligent PW (IPW) can be challenging and can pose safety concerns while not fully meeting user needs. A literature search was conducted in July 2024, using three databases: Scopus, IEEE Xplore, and Google Scholar. Articles were included if they reported improvements in IPWs based on AI techniques and user-centered design. Technological advancements based on AI techniques will allow IPWs to offer a better quality of life to their users by addressing the challenges they face in real settings. This scoping review found that efforts are being made to provide tools for route navigation, train users to operate IPWs in various situations, offer multiple control options, and improve comfort while preventing pressure ulcers due to limited mobility.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".