Wearable Triboelectric Sensor Fabricated with Cooperative Jet Printing and Magnetization-Induction Method for Human Gait Monitoring

Wearable electronic devices have brought many opportunities and hold great promise for applications in foot health monitoring. However, effective foot monitoring often requires more objective and cost-effective solutions. Here, we present a wearable triboelectric nanogenerator with a magnetic microcone array (MMA-TENG), designed for human gait monitoring. The microcone array is fabricated using a novel cooperative jet printing and magnetization-induction method, offering advantages in terms of cost-effectiveness, mass production, repeatability, and excellent structural uniformity. The fabrication parameters can be precisely optimized to control the geometry of the cone structures, thereby enhancing the device's output performance. The MMA-TENG demonstrates excellent performance in long-term pressure sensing with high sensitivity and a fast response. A deep learning-enabled smart insole system is developed using an insole with four MMA-TENGs, a ConvNeXT-based deep learning algorithm, and a portable circuit board. This system can achieve accurate personal identification and activity monitoring with recognition accuracies of 99.74% and 99.54%, respectively. Additionally, the smart insole system is also applied for flatfoot detection with an accuracy of 99.35%. The proposed MMA-TENG opens up a promising avenue for mass production and highlights its potential for supporting individuals in maintaining and monitoring their foot health effectively.

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Publication Details

Journal
ACS Sensors
Published
2025-10-14
DOI
https://doi.org/10.1021/acssensors.5c02537
Citations
1
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
0.67
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article

Wearable Triboelectric Sensor Fabricated with Cooperative Jet Printing and Magnetization-Induction Method for Human Gait Monitoring

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1 citations
ACS Sensors
Advanced Sensor and Energy Harvesting Materials
0.67
article

Wearable Triboelectric Sensor Fabricated with Cooperative Jet Printing and Magnetization-Induction Method for Human Gait Monitoring

Shudong Yu, Kuan He, Yuan Wan, Qiang Lin, Hui Li, Bei Wang, Bin Liu, Weihong Zeng, Zhaoya Chen
article en
1 citations

Abstract

Wearable electronic devices have brought many opportunities and hold great promise for applications in foot health monitoring. However, effective foot monitoring often requires more objective and cost-effective solutions. Here, we present a wearable triboelectric nanogenerator with a magnetic microcone array (MMA-TENG), designed for human gait monitoring. The microcone array is fabricated using a novel cooperative jet printing and magnetization-induction method, offering advantages in terms of cost-effectiveness, mass production, repeatability, and excellent structural uniformity. The fabrication parameters can be precisely optimized to control the geometry of the cone structures, thereby enhancing the device's output performance. The MMA-TENG demonstrates excellent performance in long-term pressure sensing with high sensitivity and a fast response. A deep learning-enabled smart insole system is developed using an insole with four MMA-TENGs, a ConvNeXT-based deep learning algorithm, and a portable circuit board. This system can achieve accurate personal identification and activity monitoring with recognition accuracies of 99.74% and 99.54%, respectively. Additionally, the smart insole system is also applied for flatfoot detection with an accuracy of 99.35%. The proposed MMA-TENG opens up a promising avenue for mass production and highlights its potential for supporting individuals in maintaining and monitoring their foot health effectively.

ACS SensorsVol. 10(10)
Sun Yat-sen University (CN), Shenzhen University (CN), The Seventh Affiliated Hospital of Sun Yat-sen University (CN), Institute of Microelectronics (CN), Huazhong University of Science and Technology (CN)
Openalex Percentile: Top 26%
Advanced Sensor and Energy Harvesting Materials
0.67
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