Low-Latency Gesture Recognition From Spatial Filtering of Single-Element Ultrasound Signals
Bibliographic record
Abstract
A-mode ultrasound (US) has been applied to detect morphological changes of skeletal muscles for gesture recognition in the human-machine interface (HMI). Reduced sensor number and latency are highly desirable in many scenarios of HMI. With a single US sensor, this paper investigated employing spatial filters on one frame of US echoes to enhance the performance of low latency gesture recognition. Common spatial pattern on depth (CSPD), adapted from common spatial pattern, was proposed to improve the separation properties of a single US frame for different hand gestures. The performance of CSPD was compared with two commonly used methods, and the results showed a significant improvement in recognition accuracy of a thirteen-class classification task. The filter coefficients were visualized to provide insights into the filtering properties of CSPD. The outcome of this work demonstrated the feasibility of employing only one frame of a single US sensor to recognize gestures with ultra-low latency, facilitating its usability in HMI applications.
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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 | 0.001 | 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 | 0.001 |
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".