ArticleMicrosystems & nanoengineering2024
Deep learning-assisted object recognition with hybrid triboelectric-capacitive tactile sensor.
Article in Microsystems & nanoengineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- A quasi-zero stiffness triboelectric vibration sensor for high-fidelity vibration characterization.Microsystems & nanoengineering · 2026Article
- Self-Driven Hybrid Piezomagnetic-Iontronic Mechanoreceptors for Bimodal SA/RA Perception and Tactile Synthesis.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Deep Learning-Based Prediction and Compensation of Performance Degradation in Flexible Sensors.Micromachines · 2026Article
- Neutral-Axis TiSensors (Basel, Switzerland) · 2026Article
- Flexible humidity sensors for diverse applications.Microsystems & nanoengineering · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Tactile sensors play a critical role in robotic intelligence and human-machine interaction. In this manuscript, we propose a hybrid tactile sensor by integrating a triboelectric sensing unit and a capacitive sensing unit based on porous PDMS. The triboelectric sensing unit is sensitive to the surface material and texture of the grasped objects, while the capacitive sensing unit responds to the object's hardness. By combining signals from the two sensing units, tactile object recognition can be achieved among not only different objects but also the same object in different states. In addition, both the triboelectric layer and the capacitor dielectric layer were fabricated through the same manufacturing process. Furthermore, deep learning was employed to assist the tactile sensor in accurate object recognition. As a demonstration, the identification of 12 samples was implemented using this hybrid tactile sensor, and an recognition accuracy of 98.46% was achieved. Overall, the proposed hybrid tactile sensor has shown great potential in robotic perception and tactile intelligence.
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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.