Enhancing Skeletal Pose Estimation from mmWave Point Clouds Through Uncertainty Reduction
Bibliographic record
Abstract
Human Pose Estimation is vital for a variety of applications, including surveillance, sports, and healthcare. Millimeter-wave (mmWave) radar technology provides significant advantages over traditional vision-based and wearable sensors, such as enhanced privacy and reduced intrusiveness. However, mmWave point clouds pose challenges due to aleatoric uncertainty from inherent noise. This study aims to enhance the mmWave-based Skeletal Pose Estimator (SPE) by reducing uncertainty. We propose a series of SPE models: (i) CSPE+: a CNN-based SPE model that uses features from nearby frames to reduce the uncertainty of SPE, which only uses a single frame, (ii) TSPE+: an enhancement of CSPE+ by replacing the CNN with the Multiscale Vision Transformer (MViT) for better temporal modeling, and (iii) CSPE++/TSPE++: further refined models using a two-stage training process with Heteroscedastic Loss in the second stage. Evaluations on two different datasets, food intake activities, and driver activities, showed significant improvements in our proposed models in pose estimation accuracy. For the Food Intake Activity Dataset, where the baseline SPE had a Mean Per Joint Position Error (MPJPE) of 64.63 mm, CSPE+ reduced the error by 56.41%, TSPE+ by 64.23%, CSPE++ by 62.22%, and TSPE++ performed best with a 68.51% reduction, achieving an MPJPE of 20.35 mm. Similarly, for the Driver Activity Dataset, where the baseline SPE had an MPJPE of 123.04 mm, CSPE+ reduced the error by 60.47%, TSPE+ by 74.82%, CSPE++ by 75.19%, and TSPE++ performed best with a 78.47% reduction, achieving an MPJPE of 26.49 mm. These results demonstrate the effectiveness of our proposed models across heterogeneous mmWave radar datasets.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".