TCubes: Non-privacy invasive dataset for activities of daily living as thermal cubes

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.