Tactile Cueing in Detecting and Controlling Pitch and Roll Motion
Bibliographic record
Abstract
BACKGROUND: Tactile cueing has been explored primarily for the detection of linear motion such as vertical, longitudinal, and lateral translation in the laboratory and in flight. The usefulness of tactile cues in detecting roll and pitch motion has not been fully investigated. METHODS: There were 12 subjects (21-56 yr) who were exposed to controlled pitch and roll motion generated by a motion platform with and without tactile cueing. The tactile system consists of a torso vest with 24 electromechanical tactors and a tactor on each shoulder and under each thigh harness, respectively. While devoid of visual and auditory cues, each subject performed three tasks: 1) indicate motion perception without tactile cues (C1); 2) return to vertical from an offset angle (C2); and 3) maintain straight and level while the platform was continuously in motion (C3). RESULTS: Our results indicated that in the absence of visual and auditory cues, subjects reported that the tactile system was useful in the execution of C2 and C3 maneuvers. Specifically, the presence of tactile cues had a significant impact on the accuracy, duration, and perceived workload. In addition, tactile cueing also increased the accuracy in returning to neutral from an offset position and in maintaining the neutral position while the platform was in continuous motion. CONCLUSIONS: Tactile cueing appears to be effective in detecting roll and pitch motion and has the potential to reduce the workload and risks of high stress and time sensitive air operations.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".