Configuration-Dependent Optimal Impedance Control of an Upper Extremity Stroke Rehabilitation Manipulandum
Bibliographic record
Abstract
repeatable tasks. Many researchers have postulated that human motor control can be mathematically represented using optimal control theories, whereby some cost function is effectively maximized or minimized. However, such abilities are compromised in stroke patients. In this study, to promote rehabilitation of the stroke patient, a rehabilitation robot has been developed using optimal control theory. Despite numerous studies of control strategies for rehabilitation, there is a limited number of rehabilitation robots using optimal control theory. The main idea of this work is to show that impedance control gains cannot be kept constant for optimal performance of the robot using a feedback linearization approach. Hence, a general method for the real-time and optimal impedance control of an end-effector-based rehabilitation robot is proposed. The controller is developed for a 2 degree-of-freedom upper extremity stroke rehabilitation robot, and compared to a feedback linearization approach that uses the standard optimal impedance derived from covariance propagation equations. The new method will assign optimal impedance gains at each configuration of the robot while performing a rehabilitation task. The proposed controller is a linear quadratic regulator mapped from the operational space to the joint space. Parameters of the two controllers have been tuned using a unified biomechatronic model of the human and robot. The performances of the controllers were compared while operating the robot under four conditions of human movements (impaired, healthy, delayed, and time-advanced) along a reference trajectory, both in simulations and experiments. Despite the idealized and approximate nature of the human-robot model, the proposed controller worked well in experiments. Simulation and experimental results with the two controllers showed that, compared to the standard optimal controller, the rehabilitation system with the proposed optimal controller is assisting more in the active-assist therapy while resisting in active-constrained case. Furthermore, in passive therapy, the proposed optimal controller maintains the position error and interaction forces in safer regions. This is the result of updating the impedance in the operational space using a linear time-variant impedance model.
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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 | 0.000 | 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 | 0.000 | 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".