TCubes: Non-Privacy Invasive Dataset for Activities of Daily Living as Thermal Cubes
Bibliographic record
Abstract
This work introduces the first open multi–infrared (IR) thermal array sensor dataset for recognizing 19 activities of daily living (ADLs) performed by 74 subjects. Each activity sample forms a 32×32×32 thermal tensor cube-hence the name TCubes. The dataset surpasses existing resources in scale, featuring the highest number of activities, twice as many sensors, and three times as many participants as comparable datasets. Thermal patterns were captured using nine low-resolution (8×8) IR thermal sensors, providing a non-invasive and privacy-preserving means of activity recognition. A comprehensive benchmarking study evaluates both convolutional and transformer-based architectures—including C3D, R(2+1)D-18, MViTv2, and Swin-T—to assess their ability to learn spatiotemporal representations from coarse thermal imagery. Results are highly promising: R(2+1)D-18 achieves the most consistent performance with an F1-score of 0.900, while transformer models such as MViTv2 and Swin-T effectively capture subtle gestures and generalize well across 18 and 19 activity classes. The lightweight 3DCNN-Mixed model further demonstrates strong efficiency for resource-constrained applications, highlighting the trade-off between accuracy and computational cost. Analyses leveraging entropy, mutual confusion, and frequency-domain representations reveal how factors such as activity location, posture, temporal dynamics, and motion periodicity influence recognition accuracy. Overall, this dataset and benchmarking suite establish a robust foundation for future research in low-resolution, low-compute, non-invasive, and privacy-preserving human activity recognition, with broad implications for eldercare, healthcare monitoring, and smart environments.
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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 | 0.001 | 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) | 0.007 | 0.006 |
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".