Robot-Assisted Rehabilitation of Ankle Plantar Flexors Spasticity: A 3-Month Study with Proprioceptive Neuromuscular Facilitation
Bibliographic record
Abstract
In this paper, we aim to investigate the effect of Proprioceptive Neuromuscular Facilitation (PNF) based rehabilitation for ankle plantar flexors spasticity by using a Robotic Ankle-foot Rehabilitation System (RARS). A modified robot-assisted system was proposed and seven post-stroke patients with hemiplegic spastic ankles participated a three-month of robotic PNF training. Their impaired sides were used as the experimental group while their unimpaired sides as the control group. A robotic intervention for the experimental group generally started from a two minutes passive stretching to warm-up or relax the soleus and gastrocnemius muscle and also ended with the same one. Then a PNF training session included 30 trails was activated between them. The rehabilitation trainings were carried out three times a week as an addition of their regular rehabilitation exercise. Passive ankle joint range of motion, resistance torque and stiffness were measured in both ankles before and after the intervention. The changes in Achilles' tendon length, walking speed, and lower limb function were also evaluated by the same physician or physiotherapist for each participant. Biomechanical measurements before interventions showed significant difference between the experimental group and the control group due to ankle spasticity. For the control group, there was no significant difference in the three months with no robotic intervention. But for the experimental group, passive dorsiflexion range of motion increased ($p0.05$). The robotic rehabilitation also improved the muscle strength ($p0.05$) and fast walking speed ($p<0.05$). These results indicated that PNF based robotic intervention could significantly alleviate lower limb spasticity and improve the motor function in chronic stroke participant. The robotic system could potentially be used as an effective tool in post-stroke rehabilitation training.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".