Industrial-grade collaborative robots for motor rehabilitation after stroke and spinal cord injury: a systematic narrative review
Bibliographic record
Abstract
Abstract Background There is a growing interest in exploring industrial-grade collaborative robots (cobots) for rehabilitation. This review explores their application for motor rehabilitation of the upper and lower extremities after a stroke and spinal cord injury (SCI). The article highlights the inherent safety features of cobots, emphasizing their design advantages over custom-built or traditional rehabilitation robots in terms of potential safety and time efficiency. Methods Database searches and reference list screening were conducted to identify studies relating to the use of cobots for upper and lower extremity rehabilitation among individuals with stroke and SCI. These articles were then reviewed and summarized. Results Thirty-three studies were included in this review. The findings suggest that the use of cobots in motor rehabilitation is still in the early stages. Some of the cobots used were equipped with sensors to detect and respond to the movement of the extremities and minimize the risk of injury. This safety aspect is crucial for patients with motor impairments. Most training protocols implemented with the cobots engaged users in repetitive task-based exercises with an overall positive user experience. Thus far, these devices have been primarily evaluated in individuals with stroke and SCI that affect the lower extremities, with no study addressing upper extremity impairments. This initial focus serves as a preliminary step toward assessing their applicability for individuals with stroke and SCI. Conclusions Cobots may have the capacity to transform therapy and support healthcare professionals in delivering more personalized and effective rehabilitation. However, there is limited evidence on their use to support upper and lower extremity rehabilitation among individuals with stroke and SCI. Further research and development are needed to refine these technologies and broaden their applications in rehabilitation settings to enhance functional recovery and overall quality of life for individuals with stroke and SCI.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".