mmWave Radar-Based Continuous Sign Language Recognition: Lightweight Modeling, Contextual Optimization, and Embedded Implementation
Bibliographic record
Abstract
Communication barriers for the hearing-impaired represent a significant societal concern. Existing sign language recognition solutions are constrained by lighting sensitivity, wearable device burdens, and limited vocabulary coverage. This work presents the first FMCW mmWave radar-based continuous sign language recognition framework, with three key innovations: (1) the Radar Continuous Chinese Sign Language-108 (RCCSL-108) dataset with 108 isolated signs and 66 natural sentences to address the critical absence of sentence-level radar data; (2) a novel lightweight lightweight Continuous Sign Language Recognition Temporal Convolutional Network (CSLR-TCN) that incorporates dual-dilated convolutions for variable-length inputs, achieving 92.66% frame accuracy and robust cross-user generalization; and (3) a novel contextual reordering module that enhances semantic understanding by 6.33% on unseen data by mitigating emotion loss in translation. The implemented embedded system achieves 87.49% recognition accuracy in untrained user experiments with ≥15 FPS throughput, marking a pivotal advancement toward practical radar-based sign language interfaces.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".