Advancements in intelligent wheelchairs: a scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it