Empirical Investigation on Practical Robustness of Keystroke Recognition Using WiFi Sensing for Future IoT Applications

Haoming Wang, Aryan Sharma, Deepak Mishra (), Aruna Seneviratne and Eliathamby Ambikairajah
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Haoming Wang: School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia
Aryan Sharma: School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia
Deepak Mishra: School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia
Aruna Seneviratne: School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia
Eliathamby Ambikairajah: School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia

Future Internet, 2025, vol. 17, issue 7, 1-27

Abstract: The widespread use of WiFi Internet-of-Things (IoT) devices has rendered them valuable tools for detecting information about the physical environment. Recent studies have demonstrated that WiFi Channel State Information (CSI) can detect physical events like movement, occupancy increases, and gestures. This paper empirically investigates the conditions under which WiFi sensing technology remains effective for keystroke detection. To achieve this timely goal of assessing whether it can raise any privacy concerns, experiments are conducted using commodity hardware to predict the accuracy of WiFi CSI in detecting keys pressed on a keyboard. Our novel results show that, in an ideal setting with a robotic arm, the position of a specific key can be predicted with 99 % accuracy using a simple machine learning classifier. Furthermore, human finger localisation over a key and actual key-press recognition is also successfully achieved, with 94 % and 89 % reduced accuracy values, respectively. Moreover, our detailed investigation reveals that to ensure high accuracy, the gap distance between each test object must be substantial, while the size of the test group should be limited. Finally, we show WiFi sensing technology has limitations in small-scale gesture recognition for generic settings where proper device positioning is crucial. Specifically, detecting keyed words achieves an overall accuracy of 94 % for the forefinger and 87 % for multiple fingers when only the right hand is used. Accuracy drops to 56 % when using both hands. We conclude WiFi sensing is effective in controlled indoor environments, but it has limitations due to the device location and the limited granularity of sensing objects.

Keywords: WiFi sensing; Channel State Information; test bed; wireless IoT devices; machine learning; keystroke detection (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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