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Personalized Keystroke Dynamics for Self-Powered Human–Machine Interfacing
Abstract
The computer keyboard is one of the most common, reliable, accessible, and effective tools used for human--machine interfacing and information exchange. Although keyboards have been used for hundreds of years for advancing human civilization, studying human behavior by keystroke dynamics using smart keyboards remains a great challenge. Here we report a self-powered, non-mechanical-punching keyboard enabled by contact electrification between human fingers and keys, which converts mechanical stimuli applied to the keyboard into local electronic signals without applying an external power. The intelligent keyboard (IKB) can not only sensitively trigger a wireless alarm system once gentle finger tapping occurs but also trace and record typed content by detecting both the dynamic time intervals between and during the inputting of letters and the force used for each typing action. Such features hold promise for its use as a smart security system that can realize detection, alert, recording, and identification. Moreover, the IKB is able to identify personal characteristics from different individuals, assisted by the behavioral biometric of keystroke dynamics. Furthermore, the IKB can effectively harness typing motions for electricity to charge commercial electronics at arbitrary typing speeds greater than 100 characters per min. Given the above features, the IKB can be potentially applied not only to self-powered electronics but also to artificial intelligence, cyber security, and computer or network access control.
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Cited by
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Self-citationTrung, T. Q., & Lee, N. (2016) · Advanced Materials · doi:10.1002/adma.201504244 · Cited by 2,160
Flexible Nanogenerators for Energy Harvesting and Self‐Powered Electronics
Fan, F. R., Tang, W., & Wang, Z. L. (2016) · Advanced Materials · doi:10.1002/adma.201504299 · Cited by 1,918
Self-citationReviving Vibration Energy Harvesting and Self-Powered Sensing by a Triboelectric Nanogenerator
Chen, J., & Wang, Z. L. (2017) · Joule · doi:10.1016/j.joule.2017.09.004 · Cited by 1,021
Self-citationChen, J., Yang, J., Li, Z., Fan, X., Zi, Y., Jing, Q., Guo, H., Wen, Z., Pradel, K. C., Niu, S., et al. (2015) · ACS Nano · doi:10.1021/acsnano.5b00534 · Cited by 632
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Metadata
- Authors
- Chen, J., Zhu, G., Yang, J., Jing, Q., Bai, P., Yang, W., Qi, X., Su, Y., & Wang, Z. L.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2015-01-02
- Volume
- 9
- Issue
- 1
- Pages
- 105-116
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2045530696
- Cited by
- 270
- References
- 0
- FWCI
- 10.43
- Type
- journal article
- Language
- en
- Open access
- closed
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- Chen, J., Zhu, G., Yang, J., Jing, Q., Bai, P., Yang, W., Qi, X., Su, Y., & Wang, Z. L. (2015). Personalized Keystroke Dynamics for Self-Powered Human–Machine Interfacing. ACS Nano. https://doi.org/10.1021/nn506832w
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