Impacts of a Robotic Arm on People with Upper-Limb Disabilities Due to Neuromuscular Disorder
Bibliographic record
Abstract
ABSTRACT Introduction Research is still limited on the impacts of robotic arms in improving independent living and social participation of individuals with upper-limb disabilities, and several knowledge gaps remain. This study aims to investigate the impacts of the JACO robotic arm on individuals with upper-limb impairments due to a neuromuscular disease. Methods For this prospective study, participants used the robotic arm in their daily life for a 2-month period. The effectiveness of the robotic arm was objectively observed during activities performed in a controlled environment. The perception of the users and of their main family caregivers was also documented. Finally, the nature and importance of activities carried out with the robotic arm in a community-living environment were explored. Results Participants' abilities with the JACO robotic arm improved continuously during the trial duration. Participants actively took part in more life habits, perceived fewer difficulties, and were more satisfied with their social participation. Despite these findings, very few life habits could be performed completely independently, limiting the impacts on global caregivers' burden. Conclusions Even if participants encountered some difficulties, this study demonstrates short-term benefits of the robotic arm for individuals with upper-limb impairments due to a neuromuscular disorder. Future studies should mainly focus on the psychosocial, economic, and occupational long-term impacts of the robotic arm. The development of services to assist the integration of such devices in people's lives could optimize their impacts. Clinical Relevance The results of this study contribute to the body of knowledge of clinicians for the prescription of a robotic arm.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".