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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".