Data Logger Technologies for Powered Wheelchairs: A Scoping Review
Bibliographic record
Abstract
In recent years, studies increasingly employed data loggers to record the objective behaviors of powered wheelchair users. Of the data logging work reported in the literature, the technologies used offer marked differences in characteristics. In order to identify and describe the extent of published research activity that relies on data logger technologies for powered wheelchairs, we performed a scoping review of the scientific and grey literature. This scoping review, complementary to a previous one related to manual wheelchairs, is part of a process aiming to help further the development and increase the functionality of data loggers with wheelchairs. Five databases were searched: Medline, Compendex, CINAHL, EMBASE, Google Scholar. Sixty papers were retained for analysis. The most frequently used technologies were all installed on the wheelchair: 19.0% were accelerometers, 14.6% were pressure sensors or switches, 13.9% were odometers, 10.9% were global positioning systems, 9.5% were tilt sensors, and 7.3% were force-sensing technologies. The most reported outcomes were pressure-relief activities (17.3%), distance traveled (9.3%), mobility events (8.9%), acceleration (8.5%), and sitting time (6.9%). Future research may be needed to assess the usefulness of different outcomes and to develop methods more appropriate to optimize the practicality of wheelchair data loggers.
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 | 0.018 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.026 | 0.028 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".