Evaluates EpiPad's gesture recognition in smartwatches, indicating enhanced usability.
With the growing popularity of smart wrist-worn devices, their small touchscreen size fundamentally limits the expressiveness of user input. Although various interaction methods have been proposed to extend input capabilities, few provide a seamless transition from traditional touchscreens and touchpads. To address this challenge, we present EpiPad, which for the first time enables multi-finger and force-sensitive input for smartwatches through mmWave-enabled on-skin touch interfaces. Leveraging a compact wrist-mounted mmWave array, EpiPad transforms the forearm's skin surface into an extended input area that preserves familiar gestures such as tapping and sliding, while enriching them with multi-finger and force interactions common in commodity touchpads. To achieve stable force estimation, we develop a novel signal extraction method based on mmWave phase dynamics and implement a recognition pipeline composed of lightweight machine learning models. Experiments with 20 users show that, after a short one-time calibration of 3 minutes 54 seconds, EpiPad achieves a gesture recognition accuracy of 92.8%, force detection accuracy of 90.2%, and ranging precision of 10.2 mm on unseen users. A real-time evaluation validates the system's reliability for real-world deployment, and a follow-up survey further confirms that EpiPad offers a low learning cost, rich gesture diversity, and an input experience closely aligned with traditional touch interfaces.
No takes yet. Share an insight, caveat, or question.
Fu et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: