TinyGaze: Lightweight Gaze-Gesture Recognition on Commodity Mobile Devices
Gaze gestures can provide hands free input on mobile devices, but practical use requires (i) gestures users can learn and recall and (ii) recognition models that are efficient enough for on-device deployment. We present an end-to-end pipeline using commodity ARKit head/eye transforms and a scaffolded guidance-to-recall protocol grounded in learning theory. In a pilot feasibility study (N=4 participants; 240 trials; controlled single-session setting), we benchmark a compact time-series model (TinyHAR) against deeper baselines (DeepConvLSTM, SA-HAR) on 5-way gesture recognition and 4-way user identification. TinyHAR achieves strong performance in this pilot benchmark (Macro F1 = 0.960 for g...
| Authors | Yaxiong Lei, Hyochan Cho, Fergus Buchanan et al. |
|---|---|
| Primary category | cs.HC |
| Published | 30 March 2026 |
| Last updated at source | 30 March 2026 |
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