Deep-learning-based unobtrusive handedness prediction for one-handed smartphone interaction
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 4941-4964 |
Journal / Publication | Multimedia Tools and Applications |
Volume | 82 |
Issue number | 4 |
Online published | 18 Jan 2022 |
Publication status | Published - Feb 2023 |
Link(s)
Abstract
The handedness (i.e. the side of the holding and operating hand) is an important contextual information to optimise the one-handed smartphone interaction. In this paper, we present a deep-learning-based technique for unobtrusive handedness prediction in one-handed smartphone interaction. Our approach is built upon a multilayer LSTM (Long-Short-Term Memory) neural network, and processes the built-in motion-sensor data of the phone in real time. Compared to the existing approaches, our approach eliminates the need of extra user actions (e.g., on-screen tapping and swiping), and predicts the handedness based on the picking-up action and the holding posture before the user performs any operation on the screen. Our approach is able to predict the handedness when a user is sitting, standing, and walking at an accuracy of 97.4%, 94.6%, and 92.4%, respectively. We also show that our approach is robust to the turbulent noise with an average accuracy of 94.6% for the situations of users in the transportation tools (e.g., bus, train, and scooter). Furthermore, the presented approach can classify users’ real-life single-handed smartphone usage into left- and right-handed with an average accuracy of 89.2%.
Research Area(s)
- Handedness prediction, LSTM, Motion sensor, Single hand, Smartphone interaction
Citation Format(s)
Deep-learning-based unobtrusive handedness prediction for one-handed smartphone interaction. / Chen, Taizhou; Zhu, Kening; Yang, Ming Chieh.
In: Multimedia Tools and Applications, Vol. 82, No. 4, 02.2023, p. 4941-4964.
In: Multimedia Tools and Applications, Vol. 82, No. 4, 02.2023, p. 4941-4964.
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review