Holding Patterns: Detecting Handedness With A Moving Smartphone At Pickup

Abstract : People often switch hands while holding their phones, based on task and context. Ideally, we would be able to detect which hand they are using to hold the device, and use this information to optimize the interaction. We introduce a method to use built-in orientation sensors to detect which hand is holding a smartphone prior to first interaction. Based on logs of people picking up and unlocking a smartphone in a controlled study, we show that a dynamic-time warping approach trained with user-specific examples achieves 83.6% accuracy for determining which hand is holding the phone, prior to touching the screen.
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Submitted on : Tuesday, November 26, 2019 - 4:46:42 PM
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Jeff Avery, Daniel Vogel, Edward Lank, Damien Masson, Hanaë Rateau. Holding Patterns: Detecting Handedness With A Moving Smartphone At Pickup. Actes de la 31e conférence francophone sur l'Interaction Homme-Machine (IHM 2019), Dec 2019, Grenoble, France. pp.7:1-7, ⟨10.1145/3366550.3372253⟩. ⟨hal-02381590⟩

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