Learning and Reproduction of Therapist’s Semi-Periodic Motions during Robotic Rehabilitation
Bibliographic record
Abstract
Summary The demand for rehabilitation services has increased in recent years due to population aging. Due to the limitations of therapist’s time and healthcare resources, robot-assisted rehabilitation is becoming an appealing, powerful, and economical solution. In this paper, we propose a solution that combines Learning from Demonstration (LfD) and robotic rehabilitation to save the therapist’s time and reduce the therapy costs when the therapy involves periodic or semi-periodic motions.We begin by modeling the therapist’s behavior (a periodic or semi-periodic motion) using a Fourier Series (FS). Later, when the therapist is no longer involved, the system reproduces the learned behavior modeled by the FS using a robot. A second goal is to combine the above with Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR) to obtain a more flexible and generalizable reproduction of the therapist’s behavior. This algorithm allows learning and imitating repetitive movement tasks. Our experimental results show the application of these algorithms to repetitive motion task.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".