The need for a paradigm shift in the development of military exoskeletons
Bibliographic record
Abstract
An exoskeleton is a body-worn mechanical device designed to work in concert with the user to enhance human capabilities. For the dismounted close combatant, an exoskeleton could be worn whilst performing a variety of complex tasks and duties. As such, there is a requirement for the human and the exoskeleton to readily adapt to different movements in different contexts. There have been many attempts to design an exoskeleton to improve the performance of the complex adaptive human system with limited success. Despite a vast investment in time and resources, exoskeletons have not yet been adopted for operational use by military leadership for use by the dismounted close combatant as they are yet to demonstrate substantive augmentation to individual warfighter and collective team capability. We argue that a major limitation of current exoskeleton systems is their inability to concurrently adapt to the user, task and environment. Unless a device can meet this requirement, it is unlikely to offer a comparative benefit to the dismounted close combatant. This paper will present the state of the art of current exoskeleton technology, and recommend future research necessary to reach an acceptable standard of augmentation and thereby lead to widespread adoption.
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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 | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".