A Review of Emerging Access Technologies for Individuals With Severe Motor Impairments
Bibliographic record
Abstract
Research and development in the field of access technologies for individuals with severe motor impairments has accelerated over the past 10 years. Many emergent alternatives to conventional mechanical switches, such as infrared sensing, electromyography, oculography, and computer vision, have been investigated for those retaining some limited volitional motor ability. At the same time, electroencephalography, electrocorticography, intracortical recordings, and electrodermal activity have been explored for those presenting as locked in. The relevant literature is scattered across many disciplines, obfuscating the strength of the clinical evidence in support of the different access technologies currently in development. This article systematically organizes the literature on the aforementioned access technologies, summarizing their underlying operational mechanisms while reviewing the clinical evidence reported between 1996 and 2006. Research evidence within this period is generally found to be at the case study or uncontrolled study level, with very modest sample sizes. Novel mechanical switches and electroencephalography-based access systems dominate the literature, whereas many other movement-based access modalities have emerged with promising early findings. Access methods for those without extant physical movement constitute a critical direction for future and ongoing research efforts.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".