Back to Search Start Over

Biometric‐Tuned E‐Skin Sensor with Real Fingerprints Provides Insights on Tactile Perception: Rosa Parks Had Better Surface Vibrational Sensation than Richard Nixon

Authors :
Senlin Hou
Qingyun Huang
Hongyu Zhang
Qingjiu Chen
Cong Wu
Mengge Wu
Chen Meng
Kuanming Yao
Xinge Yu
Vellaisamy A. L. Roy
Walid Daoud
Jianping Wang
Wen Jung Li
Source :
Advanced Science, Vol 11, Iss 34, Pp n/a-n/a (2024)
Publication Year :
2024
Publisher :
Wiley, 2024.

Abstract

Abstract The dense mechanoreceptors in human fingertips enable texture discrimination. Recent advances in flexible electronics have created tactile sensors that effectively replicate slowly adapting (SA) and rapidly adapting (RA) mechanoreceptors. However, the influence of dermatoglyphic structures on tactile signal transmission, such as the effect of fingerprint ridge filtering on friction‐induced vibration frequencies, remains unexplored. A novel multi‐layer flexible sensor with an artificially synthesized skin surface capable of replicating arbitrary fingerprints is developed. This sensor simultaneously detects pressure (SA response) and vibration (RA response), enabling texture recognition. Fingerprint ridge patterns from notable historical figures – Rosa Parks, Richard Nixon, Martin Luther King Jr., and Ronald Reagan – are fabricated on the sensor surface. Vibration frequency responses to assorted fabric textures are measured and compared between fingerprint replicas. Results demonstrate that fingerprint topography substantially impacts skin‐surface vibrational transmission. Specifically, Parks' fingerprint structure conveyed higher frequencies more clearly than those of Nixon, King, or Reagan. This work suggests individual fingerprint ridge morphological variation influences tactile perception and can confer adaptive advantages for fine texture discrimination. The flexible bioinspired sensor provides new insights into human vibrotactile processing by modeling fingerprint‐filtered mechanical signals at the finger‐object interface.

Details

Language :
English
ISSN :
21983844 and 20240023
Volume :
11
Issue :
34
Database :
Directory of Open Access Journals
Journal :
Advanced Science
Publication Type :
Academic Journal
Accession number :
edsdoj.b9bbc9bd34ce44f4b659e9ad5937d258
Document Type :
article
Full Text :
https://doi.org/10.1002/advs.202400234