Prediction of Human Postural Response in Shipboard Environments Using Multibody Dynamics and Sensory-Based Control
Bibliographic record
Abstract
Accurate prediction of the human response to ship motion can lead to improved safety and efficiency of ship operation and design.The objective of this thesis is to derive and validate a human postural stability model having similar dynamic response to an actual human when exposed to six-degree-of-freedom ship motion.The human body is modelled as a four-link inverted pendulum, which allows for representative motion of the ankles, knees, waist, and neck.Human postural stability experiments were performed during an eight day heavy-weather sea trial in the North Atlantic Ocean.This was the first known attempt to use a variety of advanced data acquisition techniques in a shipboard environment to record human sensory stimuli.This included using two full-body motion capture systems to record body segment positions and orientations, instrumented shoe insoles to measure somatosensory data, an inertial sensor to measure head vestibular data, and a head-mounted camera to capture visual proprioceptive data.The separate data sets were combined in order to describe the motion of each subject's centre of mass and centre of force within their base of support.Human postural reactions during the sea trial were correlated with the ship motion in order to derive control gains for the inverted pendulum model which included both open-loop and closed-loop components.Simulation results were compiled from 49 test cases and it was observed that the derived controller matched human response frequently in both postural roll and pitch.Further analysis indicated that there was a consistent relationship between the accuracy of the model's motions and the direction of greater ship angular motions.The specific contributions of this thesis include the spatial postural stability model, the detailed biometric data gathered during the sea trial, the control system developed by correlating ship motions with human response, and the compilation of comprehensive data sets of postural stability parameters of humans experiencing six-degree-of-freedom motion.
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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) | 0.001 | 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".