Model predictive controller–based spatiotemporal path tracking method for transhumeral prostheses
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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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".