Classification of Individual Finger Flexions Using Ultrasound Radiofrequency Signals
Bibliographic record
Abstract
The objective of this thesis is to develop a wearable human machine interface (HMI) to classify individual finger flexions using ultrasound transducers (UTs).To study the effect that lateral resolution has on the finger classification performance, 127 ultrasound radiofrequency (RF) signals are acquired from a 40 mm width linear array probe.The acquired signals were laterally averaged to show an estimated number of 4 -8 reconstructed RF signals can maintain high accuracies to that of full resolution.The pattern classification pipeline of this study is verified to make accurate finger predictions on 100 ms time intervals.The proposed spatial feature extraction method novel to ultrasound-based studies is 10 times faster to execute than conventional methods.Finally, three 280µm thin wearable UTs are attached to the forearm achieving a classification accuracy of 97.5 ± 2.9%.The results of this thesis provide the guidelines to design for wearable HMI applications.vii 5.3 Results .....
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".