Control of artificial human finger using wearable device and adaptive network-based fuzzy inference system
Bibliographic record
Abstract
This paper demonstrates a new approach for the use of multiple strain sensors on a wearable flexible finger band to measure the posture and movement of a human finger accurately. The system is further developed to repeat the human finger motion on a robotic finger. Here, we used adaptive network-based fuzzy interface system (ANFIS) to relate the strain sensor readings to human finger posture and motion. The input and output measurements used to train ANFIS are obtained from the strain sensors of the wearable platform and a 3 degree of freedom (DOF) exoskeleton testbed, respectively. The ANFIS model is then used to predict human finger posture and motion directly from the strain sensors installed on the finger band. We made additional experiments and generated testing data using the exoskeleton testbed to verify the ANFIS model. Finally, we demonstrate that the robotic finger closely follows the human finger motion by reading the wearable finger band output and calculating the posture and motion parameters in real 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 | 0.000 | 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) | 0.001 | 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".