An adaptive 4-week robotic training program of the upper limb for persons with multiple sclerosis
Bibliographic record
Abstract
It is suggested that repetitive movements can initiate motor recovery and improve motor learning in populations with neurological impairments and this process can be optimized with robotic devices. The repetitive, reproducible and high dose motor movements that can be delivered by robotics have shown positive results in functional outcomes in stroke patients. However, there is little research on robotic neurorehabilitation for persons with multiple sclerosis (PwMS), more specifically there is lack of literature with focus on the upper extremity. Therefore, the purpose of this work was to use a robotic device to implement an adaptive training program of the forearm and wrist for PwMS. This approach is unique, as it incorporates real time learning from the robotic device to alter the level of assistance/resistance to the individual. This methodology is novel and could prove to be an effective way to properly individualize the therapy process with correct dosage and prescription. 7 individuals with varying levels of MS, placed their most affected limb (forearm) on a robotic device (Wristbot), grasped the handle, and using real-time visual feedback, traced a Lissajous curve allowing the wrist to move in flexion/extension, radial/ulnar directions. Robotic training occurred 3 times per week for 4 consecutive weeks and included 40 minutes of work. Robotic software was adaptive and updated every 3 laps to evaluate the average kinematic performance which modified the robotic assistance/resistance. Outcome measures were taken pre and post intervention. Improvements in performance were quantified by average tracking and figural error, which was significantly reduced from pre – post intervention. Isometric wrist strength and grip force endurance also significantly improved from pre to post intervention. However, maximum grip force, joint position matching, 9-hole peg test, and patient-rated wrist evaluation did not show any significant improvements. To our knowledge, this study was the first adaptive and individualized robotic rehabilitation program providing two opposing forces to the hand/wrist for PwMS. Results of this 4-week training intervention, provide a proof-of-concept that motor control and muscular strength can be improved by this rehabilitation modality. This work acts as a stepping-stone into future investigations of robotic rehabilitation for an MS population.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".