Assistive feeding robot for upper limb impairment—Testing and validation
Bibliographic record
Abstract
A personal care robotic system has been developed that can provide feeding assistance to those suffering from upper limb impairment. The system introduces a novel approach for feeding that prioritizes two ideas: generalized functionality to encompass multiple feeding tasks and seamless user interaction. Additionally, the system leveraged novel computer vision ideas to incorporate functionality that was not reported in the literature. For the functional prototype, the system was comprised of an off-the-shelf six degrees of freedom robotic manipulator, two depth cameras, and an electric gripper. Furthermore, various tools used during the operation were designed and constructed using a 3D printer. The system’s software has three main operation phases: food identification, acquisition, and delivery. One of the novel features of this system is that instead of attempting to identify the food, the robot identifies the method required for acquiring the food. During testing and validation, it was found that the system had minimal identification errors, high success rates for acquisition and delivery, and a fast safety response time.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".