Layer-by-LayerSelf-Assembled Honeycomb StructureFlexible Pressure Sensor Array for Gait Analysis and Motion PostureRecognition with the Assistance of the ResNet-50 Neural Network

With the rapid emergence of flexible electronics, flexible pressure sensors are of importance in various fields. In this study, a dopamine-modified melamine sponge (MS) was used to prepare a honeycomb structure of carbon black (CB)/MXene-silicone rubber (SR)@MS flexible pressure sensor (CMSM) through layer-by-layer self-assembly technology. Using SR as a binder to construct the honeycomb structure not only improves the mechanical properties of the sensor but also provides more attachment sites for CB/MXene, enhancing the stability of the conductive network. The honeycomb structure CMSM flexible pressure sensor exhibits high sensitivity (7.44 kPa<sup>–1</sup>), a wide detection range (0–240 kPa), short response/recovery times (150 ms/180 ms), and exhibits excellent stability. In addition, a flexible smart insole has been developed based on a 6-unit CMSM sensor array, achieving plantar pressure detection. By combination of the ResNet-50 neural network algorithm with plantar pressure data under different postures, the recognition of 16 types of human motion postures has been achieved, with an accuracy rate of 90.63%. This study proposes a flexible sponge pressure sensor with excellent mechanical performance and sensing capabilities, providing new ideas and references for the design of flexible wearable sensor devices.

Publication Details

Journal
Figshare
Published
2025-03-11
DOI
https://doi.org/10.1021/acssensors.5c00187.s001
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Layer-by-LayerSelf-Assembled Honeycomb StructureFlexible Pressure Sensor Array for Gait Analysis and Motion PostureRecognition with the Assistance of the ResNet-50 Neural Network

Figshare
Advanced Sensor and Energy Harvesting Materials
article

Layer-by-LayerSelf-Assembled Honeycomb StructureFlexible Pressure Sensor Array for Gait Analysis and Motion PostureRecognition with the Assistance of the ResNet-50 Neural Network

article en

Abstract

With the rapid emergence of flexible electronics, flexible pressure sensors are of importance in various fields. In this study, a dopamine-modified melamine sponge (MS) was used to prepare a honeycomb structure of carbon black (CB)/MXene-silicone rubber (SR)@MS flexible pressure sensor (CMSM) through layer-by-layer self-assembly technology. Using SR as a binder to construct the honeycomb structure not only improves the mechanical properties of the sensor but also provides more attachment sites for CB/MXene, enhancing the stability of the conductive network. The honeycomb structure CMSM flexible pressure sensor exhibits high sensitivity (7.44 kPa<sup>–1</sup>), a wide detection range (0–240 kPa), short response/recovery times (150 ms/180 ms), and exhibits excellent stability. In addition, a flexible smart insole has been developed based on a 6-unit CMSM sensor array, achieving plantar pressure detection. By combination of the ResNet-50 neural network algorithm with plantar pressure data under different postures, the recognition of 16 types of human motion postures has been achieved, with an accuracy rate of 90.63%. This study proposes a flexible sponge pressure sensor with excellent mechanical performance and sensing capabilities, providing new ideas and references for the design of flexible wearable sensor devices.

Figshare
Affordable and clean energy
Openalex Percentile: Top 58%
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.