Denoising UWB Radar Data for Human Activity Recognition Using Convolutional Autoencoders
Bibliographic record
Abstract
Human Activity Recognition (HAR) is one of the most popular research topics thanks to its usefulness in providing targeted, meaningful assistance to older adults. Because of the aging of the population in first-world countries, it becomes increasingly important to find innovative solutions that reduce risks associated with aging-in-place policies. HAR proposes solutions that are based on Ambient Intelligence (AmI) to alleviate those risks. In this work, we exploited three UWB radars to recognize 14 activities performed by 19 participants in a prototype smart-home apartment. The main contribution of this paper is UWB radar data cleaning on a practical dataset. The UWB radar data has been filtered using an unsupervised deep convolutional autoencoder (CNN-AE) that learns background noise from the data. This filtering method is compared to the unfiltered data using a Convolutional Neural Network (CNN) classifier in a Leave-One-Subject-Out (LOSO) classification. Performances attest that the CNN-AE unsupervised filtering is efficient for HAR. In addition, we tested the generalization potential of this architecture when the dataset is comprised of a lower number of participants (1, 5, 10, and all 19 participants). Generalization in HAR is difficult as the results show the importance of data quantity and number of subjects. We obtained 69.9% top-1 accuracy when using our filtering architecture compared to 48.4% without it. To conclude, we show that an unsupervised CNN-AE can efficiently filter and generalize UWB radar data in a HAR setting while providing easier learning constraints and implementation on a practical dataset.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".