Towards the adoption of wearable exoskeletons in occupational workspaces: model-based assessment and control of back-support exoskeletons
Bibliographic record
Abstract
Material handling activities such as dynamic lifting of heavy objects or prolonged forward bending postures are common in occupational workspaces such as factories, warehouses or nursing homes. These movements have been identified as risk factors for the development of musculoskeletal disorders such as low back pain. Back-support exoskeletons have been demonstrated to relieve spinal loading in the lower back musculoskeletal system. However, the adoption of back-support exoskeletons in real-life scenarios is minimal due to several factors. For instance, most models are unable to adapt their assistance levels to workers’ multiple and diverse tasks. Additionally, there is a lack of benchmarking guidelines which allow the correct assessment and comparison of different models. This dissertation aims at enhancing the adoption of back-support exoskeletons. First, we developed a set of tools to evaluate how the human musculoskeletal system reacts to the support provided by active and passive back-support exoskeletons. In a first study, we used objective and subjective measures to assess the support provided by two rigid and two soft passive back-support exoskeletons. This study proposed, therefore, a set of benchmarking guidelines which aim at standardizing the evaluation of these devices. Additionally, in this dissertation, we proposed electromyography-driven musculoskeletal modeling techniques to assess the biomechanical impact of back-support exoskeletons in terms of lumbosacral joint moments and compression forces. This was done under a large repertoire of symmetric, asymmetric and weight conditions, both offline and in real-time. Specifically, the proposed real-time pipeline demonstrated its potential for the development of real-time biofeedback frameworks to assess injury-related risk factors and back-support exoskeletons. Finally, we developed a novel and intuitive human-machine interface for an active back-support exoskeleton. This interface relied on our real-time electromyography-driven methodology and derived assistive forces proportional to biological lumbosacral joint moments. This resulted in adaptive assistive forces, specific to the lifting technique, external loading conditions and actual internal forces of the user’s musculoskeletal system. Overall, the studies included in this dissertation have the potential to facilitate the translation of wearable assistive robotic exoskeletons to real-life occupational environments.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".