The impact of bimanual reach training with augmented position sense feedback on post-stroke upper limb somatosensory and motor impairment
Bibliographic record
Abstract
BACKGROUND: Impaired arm position sense is a common somatosensory impairment after stroke, which significantly impacts the performance of functional activities using the upper limb. However, few clinical interventions target loss of position sense after stroke. Our aim was to use interlimb force-coupling to augment position sense of the stroke-affected arm during a bilateral reaching task and investigate the impact of training with this feedback manipulation on measures of arm position matching ability and both bilateral and unilateral motor control. METHODS: Twenty-four participants with a history of stroke were randomized (N = 12/group) to perform mirrored bimanual aiming movements with either interlimb force-coupling (Augmented PF) or uncoupled symmetrical reaches with only visual feedback about movement position. Participants completed 11 sessions (295 bimanual reaches/session) using a Kinarm End-Point robot. Performance on measures of arm position sense (Arm Position Matching, APM), motor impairment (Fugl-Meyer Upper Limb, FM), motor function (Wolf Motor Function Test, WMFT), unilateral reach accuracy and speed (Visually Guided Reaching, VGR), and bilateral reach symmetry were collected before and after training to characterize changes in upper limb somatosensory and motor control performance. RESULTS: APM Task Scores improved for both groups. This improvement was specifically observed through reduced APM variability, but not accuracy. FM scores also improved for both groups. The group that did not practice with force-coupling between limbs improved on measures of bilateral movement symmetry on a mirrored reaching task and had faster VGR movement times in post-test. CONCLUSION: Symmetrical reach training with or without augmented PF led to reduced motor impairment and benefited upper limb position matching ability by reducing APM variability. Augmenting position sense during reaching did not provide additional benefits for position matching accuracy. Advantages for unilateral movement speed and bilateral reach symmetry measures in the group that practiced without interlimb coupling may reflect specificity of practice effects due to similarity between test and training conditions for this group.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".