A touch-based multimodal and cryptographic bio-human-machine interface

17Citations
Citations of this article
30Readers
Mendeley users who have this article in their library.
Get full text

Abstract

The awareness of individuals' biological status is critical for creating interactive and adaptive environments that can actively assist the users to achieve optimal outcomes. Accordingly, specialized human-machine interfaces-equipped with bioperception and interpretation capabilities-are required. To this end, we devised a multimodal cryptographic bio-human-machine interface (CB-HMI), which seamlessly translates the user's touch-based entries into encrypted biochemical, biophysical, and biometric indices. As its central component, the CB-HMI features thin hydrogel-coated chemical sensors and inference algorithms to noninvasively and inconspicuously acquire biochemical indices such as circulating molecules that partition onto the skin (here, ethanol and acetaminophen). Additionally, the CB-HMI hosts physical sensors and associated algorithms to simultaneously acquire the user's heart rate, blood oxygen level, and fingerprint minutiae pattern. Supported by human subject studies, we demonstrated the CB-HMI's capability in terms of acquiring physiologically relevant readouts of target bioindices, as well as user-identifying and biometrically encrypting/decrypting these indices in situ (leveraging the fingerprint feature). By upgrading the common surrounding objects with the CB-HMI, we created interactive solutions for driving safety and medication use. Specifically, we demonstrated a vehicle-activation system and a medication-dispensing system, where the integrated CB-HMI uniquely enabled user bioauthentication (on the basis of the user's biological state and identity) prior to rendering the intended services. Harnessing the levels of bioperception achieved by the CB-HMI and other intelligent HMIs, we can equip our surroundings with a comprehensive and deep awareness of individuals' psychophysiological state and needs.

Cite

CITATION STYLE

APA

Lin, S., Zhu, J., Yu, W., Wang, B., Sabet, K. A., Zhao, Y., … Emaminejad, S. (2022). A touch-based multimodal and cryptographic bio-human-machine interface. Proceedings of the National Academy of Sciences of the United States of America, 119(15). https://doi.org/10.1073/pnas.2201937119

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free