Full-Body Haptic Cueing for Augmented Pilot Perception in Position/Velocity Tracking Tasks
Bibliographic record
Abstract
This paper investigates the use of multi-modal cueing through full-body haptic feedback to enhance pilot-vehicle system (PVS) performance, reduce mental workload (MWL), and increase situational awareness (SA) in both good and degraded visual environments (GVE/DVE). Piloted simulations were conducted using an H-60-like flight dynamics model in a virtual reality (VR) motion-based simulator, evaluating two ADS-33-like mission task elements (MTEs) – precision hover and slalom – under visual-only and combined visual and haptic feedback conditions in both GVE and DVE. The H-60 flight dynamics were augmented with a dynamic inversion (DI)- based stability augmentation system (SAS), implementing rate-command/attitude hold (RCAH) response type on the roll, pitch, and yaw axes and altitude hold response type on the vertical axis. The SAS was designed to achieve Level 1 handling qualities per ADS-33 standards. The full-body haptic cueing strategy leveraged an outer-loop DI control law, which provided vibrotactile feedback to cue desired roll, pitch, and yaw attitudes to the pilot. Roll cues were delivered via tactors mounted on the upper arms, pitch cues via tactors on the chest and back, and yaw cues via tactors on the calves. Eight test subjects participated in the piloted simulations, including three U.S. Navy test pilots and five subjects with different flying experiences. Results indicated that haptic feedback significantly improved hover performance, reducing MWL and enhancing SA, particularly in DVE. However, in the slalom task, predefined haptic guidance misaligned with pilots’ individual control strategies, leading to performance degradation. This finding highlights the need for pilot-specific adaptive haptic feedback to mitigate inconsistencies in dynamic maneuvering tasks.
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".