Optical and inertial motion capture joint angle comparison using Jack™
Bibliographic record
Abstract
Current digital ergonomic simulation processes utilize a single posture within a workstation to evaluate the risk of injury, however there is a desire from the manufacturing industry to move towards full dynamic human ergonomic simulations. These dynamic simulations would benefit ergonomists and engineers by allowing for evaluation of tasks completed within an entire workstation. However, dynamic simulations require a great time commitment, on behalf of the user, to complete. Motion capture technology can be used to reduce the users time; however, the gold standard optical-based technology is limited to laboratory-based examinations. Inertial-based capture technologies might be a solution as it would allow for direct capture of the workers within the manufacturing environment, however this technology needs to be assessed for accuracy. Twenty participants completed four multi-task events simulating real work, in a laboratory, while instrumented with inertial and optical based motion capture systems. The collected kinematic data was used to drive the motions of a digital Jack™ manikin within its digital environment, and comparisons between joint angles produced from the software were conducted between the optical system and two inertial systems. Results indicate significant joint angle error relative between the optical system and the inertial systems, however, one of the systems showed less error than the other. These errors can impact overall accuracy and representation of work within a human modeling environment.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".