Preliminary results for a force feedback bimanual rehabilitation system
Bibliographic record
Abstract
Stroke, a cardiovascular disease, is causing an increasingly heavy financial and medical load in modern societies. Yet the current therapeutic alternatives leave many individuals with stroke with limited ability to sustain an independent lifestyle. In turn, stroke survivors' quality of life is significantly diminished. Physical therapy is considered the main course of treatment for individuals with chronic stroke. Predominantly, research in rehabilitation investigates methods aiming to maximize the treatment benefits to patients. A novel Bimanual Wearable Robotic Device (BWRD) and protocol are proposed for the rehabilitation of stroke patients. The BWRD is composed of Master and Slave devices for the non-paretic and paretic upper limbs respectively. While the non-paretic arm controls the paretic arm in position, force feedback from the paretic arm, providing an indication of impairment, is sent back to the non-paretic arm. In this preliminary study, three participants with chronic unilateral stroke were recruited to participate in a total of three sessions per participant. To evaluate the effects of the BWRD, participants underwent pre and post training functional assessments. In addition we explored the neurobiological effects of this therapy using indices of brain excitability (Transcranial Magnetic Stimulation (TMS)), brain activity (Electroencephalography (EEG)) and muscle activation (Electromyography (EMG)). This work presents preliminary results. Following bimanual training, we measured an increase in excitability of both hemispheres as well as reduced inhibition as measured by Transcallosal Inhibition Laterality Index (TCI LI). The presented data supports and validates the approach implemented with the BWRD.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".