Impacts of a lower limb exoskeleton robot on the muscle strength of tibialis anterior muscle in stroke patients
Bibliographic record
Abstract
This work aims to explore the impact of a proposed lower limb exoskeleton robot on the muscle strength of the tibialis anterior muscle in stroke patients. Firstly, 24 patients with stroke hemiplegia were divided into the robot group and the control group according to a random number table. Both groups received conventional rehabilitation treatments. Moreover, the robot group took the walking training with UG0210, a lower limb exoskeleton walking rehabilitation device developed by the Hangzhou RoboCT Technology Development Co., Ltd., once per day, 30 minutes per time, a total of 20 times of treatment. The control group took the conventional rehabilitation walking training, once per day, 30 mins per time, a total of 20 times of treatment. At the beginning of the trial, the manual muscle strength test (MMT) was used to assess the pre-trial muscle strength within the trial cycle. The efficacy of the two groups was compared. Results The muscle strength of the tibialis anterior muscle was higher than that without treatments in both groups (P<0.05). The curative effect of the robot group was better than that of the control group (P<0.05). Conclusions With the help of the designed lower limb exoskeleton robot, both tibialis anterior muscle strength and lower limb motor function of stroke patients were improved compared to the control group. The comparison shows the attractive potential and value of the robot assisted rehabilitation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".