Handwritten Digit Leakage from Smartphone Motion Sensors Across Unseen Users and Phone Models

Abstract:Smartphone motion sensors support interactive applications, but their readings may also reveal touchscreen input beyond their intended use. Assuming known drawing intervals, we study whether handwritten digits remain predictable across users and devices, as a 10-class problem on 19,628 HuMIdb recordings from 481 participants. We compare handcrafted features with classical machine learning algorithms, MiniRocket kernels, and a compact sensor patch transformer on accelerometer, linear acceleration, gyroscope, and gravity signals. The transformer achieves 57.74\% accuracy and 82.64\% top-3 accuracy on 75 unseen participants, and 58.77$\pm$0.95\% over 3 seeds for unseen participants on 9 unseen phone models. Low motion recordings remain informative, accuracy is not monotonic in motion level, and the tested contrastive pretraining, augmentation, and derived signals give no consistent gains. Digits are thus predictable beyond familiar users and phone models under assumed segmentation, while acquisition-order shortcuts limit conclusions about practical privacy exposure. Code available at: this https URL.

7 pages, 4 figures, 3 tables, 7 numbered equations, and 33 references. Code available at this https URL

Machine Learning (cs.LG); Human-Computer Interaction (cs.HC)

arXiv:2609.32117 [cs.LG]

(or arXiv:2609.32117v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.32117 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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