Advancing Home Rehabilitation: The PlanAID Robot’s Approach to Upper-Body Exercise Through Impedance Control
Bibliographic record
Abstract
Rehabilitation robots are a leading solution towards bridging the gap between the growing number of rehabilitation patients requiring therapy and the limited availability of healthcare professionals. However, existing robotic systems are often bulky and expensive, limiting their ability to provide widespread, repetitive, and intensive exercises. This paper presents the development of an impedance-based control strategy designed to provide safe and compliant upper-body passive and active exercises on the low-cost PlanAID robot, which is built using consumer-grade components. The system's functionalities are evaluated using a high-precision force sensor. Results show that the PlanAID exhibits performance comparable to seminal devices such as the MIT-Manus, achieving a similar applicable reaction force target of 28 N and reflected inertia of 1.1 kg. Although the overall performance is comparable, the low-cost PlanAID prototype suffers from reduced coupled stability margins, limiting the maximum achievable virtual spring constant to 1100 N/m. Despite this limitation, the stiffness values required in practical applications remain low, suggesting that the PlanAID could potentially be a viable candidate for real-world rehabilitation. Initial user feedback was obtained through a preliminary qualitative trial involving healthy subjects.
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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".