Memory-Efficient High-Accuracy Food Intake Activity Recognition with 3D mmWave Radars
Bibliographic record
Abstract
Non-invasive and privacy-preserving recognition of food intake activities has applications in diet management, telecare, and smarthome data monetization. In this paper, we tackle the challenging problem of recognizing food intake activity using sparse point clouds captured by a single mmWave radar, which is non-invasive and privacy-preserving. We propose: (i) an enhanced Skeletal Pose Estimator (SPE) capable of generating more precise skeletons for food intake activity recognition, outperforming the state-of-the-art MARS with an average reduction of 45.16% in the error of the estimated joints, (ii) a Dynamic Point Cloud Recognizer (DPR), which adapts SPE to directly process dynamic point clouds for food intake activity recognition, outperforming the state-of-the-art FIA with a 4.10% enhancement in classification accuracy and a 78.29% reduction in memory consumption, and (iii) a Lightweight Dynamic Point Cloud Recognizer (LDPR), which eliminates the need for CNNs, hence reducing model complexity and outperforming DPR by 0.15% in classification accuracy and a 42.80% reduction in memory consumption. In addition to food intake activity recognition, the skeletons generated by our SPE can also be used to recognize other fine- and coarse-grained activities for applications like rehabilitation, driver monitoring, and fitness assistance.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".