ArticleNature communications2024
Behavioral biometric optical tactile sensor for instantaneous decoupling of dynamic touch signals in real time.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Convergence of Soft Electronics and Artificial Intelligence: From Materials to Intelligent Systems.Nano-micro letters · 2026Review
- Multiscale-structured miniaturized 3D force sensors.Nature materials · 2026Article
- Moisture-driven, self-powered noncontact sensing interfaces via turbulence-tailored hygroelectronic effect.Science advances · 2026Article
- Recent Advances in Decoupling Strategies for Soft Sensors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Intelligent Sensing: The Emerging Integration of Machine Learning and Soft Sensors Based on Hydrogels and Ionogels.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Ultrathin Soft Wearable Sensor Materials and Structures: A Review of Current Trends and Prospectives.ACS applied materials & interfaces · 2025Review
- Stable Open-Shell Conjugated Terpolymer with Extended NIR Absorption for Organic Photodetectors Detecting Beyond 1000 nm.ACS applied materials & interfaces · 2025Article
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
Decoupling dynamic touch signals in the optical tactile sensors is highly desired for behavioral tactile applications yet challenging because typical optical sensors mostly measure only static normal force and use imprecise multi-image averaging for dynamic force sensing. Here, we report a highly sensitive upconversion nanocrystals-based behavioral biometric optical tactile sensor that instantaneously and quantitatively decomposes dynamic touch signals into individual components of vertical normal and lateral shear force from a single image in real-time. By mimicking the sensory architecture of human skin, the unique luminescence signal obtained is axisymmetric for static normal forces and non-axisymmetric for dynamic shear forces. Our sensor demonstrates high spatio-temporal screening of small objects and recognizes fingerprints for authentication with high spatial-temporal resolution. Using a dynamic force discrimination machine learning framework, we realized a Braille-to-Speech translation system and a next-generation dynamic biometric recognition system for handwriting.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.