Model predictive controller–based spatiotemporal path tracking method for transhumeral prostheses
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Transhumeral prostheses are worn by transhumeral amputees to replace the missing upper limb segment between shoulder and elbow. Prostheses should be able to function as a natural limb for the user to gain the full advantage of wearing a prosthesis. When performing reach-to-grasp and pointing motions by the upper limb, the hand is capable of adhering to a straight-line path with a bell-shaped velocity profile. AIM: Aim was to develop a dynamic path-tracking method for transhumeral prostheses to gain the capability of adhering to a straight-line path. METHOD: Proposed method uses model predictive controller (MPC) developed based on the kinematic model of the prosthesis. Moreover, a shoulder matcher is proposed to match actual shoulder pose with the predicted shoulder pose and to select the best joint angles for the prosthesis for a particular instance. Furthermore, the proposed method is capable of dynamically updating the path if the human performs shoulder motions, which are not as planned by the MPC. RESULTS: Several experiments are conducted to validate the proposed method. The proposed method is capable of taking a straight-line path similar to a natural human. CONCLUSION: This paper proposed a dynamic path-tracking method based on a model predictive controller. The proposed method is capable of taking the prosthetic hand on a straight-line path, which is similar to a path taken by a natural human hand.
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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 | 0.001 | 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 | 0.000 | 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