Effect of Lateral Resolution on Classifying Individual Finger Flexions using Ultrasound
Bibliographic record
Abstract
B-mode ultrasound imaging has recently shown promise in achieving higher classification accuracies than sur-face electromyography for predicting discrete hand gestures and individual finger movements. This preliminary study inves-tigates the performance in classifying finger flexions when re-ducing the lateral sampling interval resolution of a conventional clinical ultrasonic imaging probe with data collected from one subject. An experiment using spatial and temporal features, ex-tracted from ultrasound radio-frequency (RF) signals are used with linear discriminant analysis to classify individual thumb, index, middle, ring and pinky finger flexion movements. The spatial lateral sampling interval is increased from 315 μm to 10 mm (reduction in lateral resolution) by averaging four groups of 32 consecutively acquired A-mode ultrasound RF signals from a 40 mm probe. The results for the four averaged RF ul-trasound signals with a 10 mm lateral sampling interval had an F1 score ranging between 77-91% with a classification accuracy of 84% for all five finger flexions. This classification accuracy was similar when using the acquired 315 μm lateral resolution and decreases to a classification accuracy of 32% for no lateral resolution, when the full 40 mm width is averaged into a single RF signal. The results show motivation for using a wearable multichannel ultrasound device for predicting individual finger flexions for prosthetic devices.
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 | 0.002 | 0.008 |
| 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 | 0.001 |
| 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".