A Therapist-Taught Robotic System for Assistance During Gait Therapy Targeting Foot Drop
Bibliographic record
Abstract
The adoption of robots in rehabilitation medicine settings has become increasingly attractive in recent years. Robots are capable of providing repetitive, high-intensity physiotherapy. In this paper, we apply kinesthetic teaching principles to a robotic system in order to allow it to first learn and then imitate a therapist's behavior while assisting a patient in a lower-limb therapy task. A therapist's assistance in lifting a patient during treadmill-based gait therapy is statistically encoded by the system using Learning-from-Demonstration (LfD) techniques. Later, the therapist's assistance is imitated by the robot, allowing the patient to continue practicing in the absence of the therapist. Preliminary experiments are performed with inexperienced users playing the role of the assisting therapist, and with healthy participants (wearing an elastic cord to simulate foot drop) playing the role of a patient. Toe clearance values are recorded, which show that the system is able to provide the full clearance needed by the patient to practice in the absence of the therapist.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".