Adaptive Control of an Upper Extremity Rehabilitation Robot with Backlash
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In Canada over 40,000 new stroke cases are reported annually [1], and it costs the Canadian health care system $3.2 billion a year Thus, new rehabilitation technologies are being investigated. A rehabilitation robot can be used to deliver repetitive practice to the stroke patients, which is the key element for motor recovery Safety issues regarding humanrobot interactions restrict selection of control scenarios. Currently admittance and impedance control approaches and their variations are used to control the rehabilitation robots To implement these control strategies, a complete and accurate dynamic model of the robotic system is required. This issue can be addressed by incorporating robust or adaptive control approaches in the above strategies. For the robust control, if the dynamic uncertainties of the robot are too great, the quality of adaptive assistance or resistance may be compromised during therapy In the adaptive control, for the convergence of the adaption law, a persistently exciting input is required In addition, both adaptive and robust controllers may need high gains when mechanical discontinuities (such as gear backlash) are introduced to the system dynamics. Hence, a precise dynamic model estimation is essential, and can be done through a proper system parameter identification approach.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it