Dual-Parameter Bionic Tactile Sensing System

In this article, we introduce for the first time a bionic tactile sensing system capable of distinguishing the hardness and roughness of objects, and design experiments to verify the effectiveness of this system. This bionic sensing system, through the simultaneous measurement of stress and vibration modalities using FBG sensing point and optical coherence tomography imaging technology, realizes the classification and identification of the hardness and roughness of the tested objects. We designed a dual-parameter tactile sensing experiment, measured six different types of objects with varying levels of roughness and hardness then we compiled a multimodal dataset. Experimental results show that this bionic tactile sensing system has a classification accuracy rate of 92.83% for these six tested objects. Additionally, through modal degradation experiments, we found that both stress and vibration modalities have significant effects on the classification accuracy rate, thereby validating the effectiveness of the bionic tactile sensing system which can be used in as telemedicine, smart industry, and smart agriculture.

Authors

Institutions

Publication Details

Journal
IEEE Internet of Things Journal
Published
2025-01-22
DOI
https://doi.org/10.1109/jiot.2025.3532650
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Dual-Parameter Bionic Tactile Sensing System

Chengang Lyu, Jiangqianyi Dai, Yanxia Du
IEEE Internet of Things Journal
Advanced Sensor and Energy Harvesting Materials
article

Dual-Parameter Bionic Tactile Sensing System

Chengang Lyu, Jiangqianyi Dai, Yanxia Du
article en

Abstract

In this article, we introduce for the first time a bionic tactile sensing system capable of distinguishing the hardness and roughness of objects, and design experiments to verify the effectiveness of this system. This bionic sensing system, through the simultaneous measurement of stress and vibration modalities using FBG sensing point and optical coherence tomography imaging technology, realizes the classification and identification of the hardness and roughness of the tested objects. We designed a dual-parameter tactile sensing experiment, measured six different types of objects with varying levels of roughness and hardness then we compiled a multimodal dataset. Experimental results show that this bionic tactile sensing system has a classification accuracy rate of 92.83% for these six tested objects. Additionally, through modal degradation experiments, we found that both stress and vibration modalities have significant effects on the classification accuracy rate, thereby validating the effectiveness of the bionic tactile sensing system which can be used in as telemedicine, smart industry, and smart agriculture.

IEEE Internet of Things JournalVol. 12(11)
Tianjin University (CN), Tianjin Hospital (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 99%
Advanced Sensor and Energy Harvesting Materials
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Dual-Parameter Bionic Tactile Sensing System — Chengang Lyu, Jiangqianyi Dai, et al. · IEEE Internet of Things Journal (2025) | TGRS Research Map | TGRS