Reducing compensatory movements in stroke therapy through the use of robotic devices and augmented feedback
Bibliographic record
Abstract
Compensatory movements are commonly employed by stroke survivors to adapt to the loss of motor function. However, their long-term use can be detrimental to post-stroke recovery of function. In this work, we focused on trunk displacement, which is a compensatory movement that stroke survivors use when reaching forward. Current therapeutic practices to reduce this tendency rely on the use of physical restraints to secure a person to a chair. An alternate approach to reduce compensation is the use of active technology that delivers augmented feedback about trunk movement. Using this methodology provides several advantages over physical restraints, such as: the person is actively involved in the planning and executing of the movement rather than relying on a physical barrier that continuously prevents trunk movement; the feedback intensity, frequency, and thresholds can easily be modified in real time; the system is less intrusive as it does not require the person to be strapped or secured to a chair by someone else; it can be used safely without direct supervision; the trunk compensation feedback can be used as a variable inside a motivating video game scenario. This dissertation is comprised of three studies to investigate: the extent of stroke survivors’ trunk displacement when reaching forward to targets at different heights (Study 1), the use of visual and force feedback (Study 2), and the importance of including game scores (Study 3) to reduce trunk compensation. The results from these studies suggest that target height influences the degree of trunk compensation of hemiparetic participants. In addition, the use of visual and force feedback to cue participants about their level of trunk compensation can lead to a reduction of this movement. Similarly, the use of game scores resulted in a reduction of trunk compensation. No feedback modality or combination was superior to another for reducing trunk displacement. The findings from this work suggest that the use of augmented feedback is a viable approach to reduce trunk compensation in hemiparetic stroke survivors. These ideas should be tested in long-term interventions before we can make a final recommendation to the rehabilitation community.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".