Sign Language Gesture Recognition Using Doppler Radar and Deep Learning
Bibliographic record
Abstract
In this paper, we study American sign language (ASL) hand gesture recognition using Doppler radar. A set of ASL hand gesture motions are captured as micro- Doppler signals using a microwave X-band Doppler radar transceiver. We apply joint time-frequency analysis and observe the presence of the micro- Doppler signatures in the spectrogram. The micro- Doppler signatures of different hand gestures are analyzed using Matlab. Each hand gesture is observed to contain unique spectral characteristics. Based on unique spectral characteristics, we investigate the classification of ASL essential short phrases including emergency signals. For recognizing and characterizing the presence of micro-Doppler signatures in spectrogram we explore deep convolution neural network (DCNN) algorithm. We show that the DCNN algorithm can classify different sign language gestures based on the presence of micro- Doppler signatures in the spectrogram with fairly high accuracy. Experimental results reveal that utilizing 80% of data for training, and the remaining 20% for validation purposes in DCNN algorithm a validation accuracy of 87.5% is achieved. To further improve the recognition system, we apply a very deep learning algorithm VGG-16 using transfer learning, which improves the validation accuracy to 95%.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".