Although many studies have attempted to detect the hand postures of a mobile device to utilize these postures as a user interface, they either require additional hardware or can differentiate a limited number of grips only if there is a touch event on the mobile device’s screen. In this paper, we propose a novel grip sensing system, called SmartGrip, which allows a mobile device to detect different hand postures without any additional hardware and a screen touch event. SmartGrip emits carefully designed sound signals and differentiates the propagated signals distorted by different user grips. To achieve this, we analyze how a sound signal propagates from the speaker to the microphone of a mobile device and then address three key challenges: sound structure design, volume control, and feature extraction and classification. We implement and evaluate SmartGrip on three Android mobile devices. With six representative grips, SmartGrip exhibits 93.1% average accuracy for ten users in an office environment. We also demonstrate that SmartGrip operates with 83.5 to 98.3% accuracy in six different (noisy) locations. Further demonstrating the feasibility of SmartGrip as a user interface, we develop an Android application that exploits SmartGrip, validating its practical usage.
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Note that the criterion volume depends on mobile devices. For Samsung Galaxy S8 and Google Pixel, the criterion volume corresponds to 60%. It is not difficult to calibrate the criterion volume for target mobile devices.
After applying FFT, we have 512 features with 0–24kHZ. We remove features with 0–16kHZ as well as additional features related to fade in & fade out, yielding 172 features.
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This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2019R1A2B5B02001794). Jinkyu Lee is the corresponding author.
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A short, preliminary version of this paper has been presented as a poster , which is 4 pages long.
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Kim, N., Lee, J., Whang, J.J. et al. SmartGrip: grip sensing system for commodity mobile devices through sound signals. Pers Ubiquit Comput 24, 643–654 (2020). https://doi.org/10.1007/s00779-019-01337-7
- Grip sensing system
- Mobile device
- Sound signals
- Sound structure design