Affordable High-Sensitivity Flexible Strain Sensor Integrated with Machine Learning for Tracking In-Air Handwriting and Leg Motion

The designed flexible fiber strain sensor is fabricated by infiltrating superconductive carbon black into the SEBS substrate, exhibiting a small volume, high gauge factor (GF), wide strain range, excellent stability, and low cost. At a strain of 550%, GF ≈ 1909.5, while the strain detection range exceeds 620%. After 2000 cycles of tensile and compressive tests, the sensor maintains remarkable stability. In air-handwriting applications, the recognition of four commonly used characters was achieved based on changes in electrical signals. To further enhance the accuracy and efficiency of signal differentiation under large strain conditions, the multifusion machine learning algorithm was integrated into leg posture detection, achieving an accuracy of 0.9854. These results strongly support the advancement of flexible sensors in intelligent control, human–computer interaction, and motion monitoring.

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Institutions

Publication Details

Journal
ACS Applied Electronic Materials
Published
2025-04-15
DOI
https://doi.org/10.1021/acsaelm.5c00033
Citations
5
Primary Topic
Tactile and Sensory Interactions
Type
article
Field-Weighted Citation Impact
7.16

Funders

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article

Affordable High-Sensitivity Flexible Strain Sensor Integrated with Machine Learning for Tracking In-Air Handwriting and Leg Motion

Xin Yan, Xuegang Li, Ning Yang, Xue Zhou et al.
5 citations
ACS Applied Electronic Materials
Tactile and Sensory Interactions
7.16
article

Affordable High-Sensitivity Flexible Strain Sensor Integrated with Machine Learning for Tracking In-Air Handwriting and Leg Motion

Xin Yan, Xuegang Li, Ning Yang, Xue Zhou, Yaping Hui, Tonglei Cheng, Weijia Wang
article en
5 citations

Abstract

The designed flexible fiber strain sensor is fabricated by infiltrating superconductive carbon black into the SEBS substrate, exhibiting a small volume, high gauge factor (GF), wide strain range, excellent stability, and low cost. At a strain of 550%, GF ≈ 1909.5, while the strain detection range exceeds 620%. After 2000 cycles of tensile and compressive tests, the sensor maintains remarkable stability. In air-handwriting applications, the recognition of four commonly used characters was achieved based on changes in electrical signals. To further enhance the accuracy and efficiency of signal differentiation under large strain conditions, the multifusion machine learning algorithm was integrated into leg posture detection, achieving an accuracy of 0.9854. These results strongly support the advancement of flexible sensors in intelligent control, human–computer interaction, and motion monitoring.

ACS Applied Electronic MaterialsVol. 7(9)
Foshan University (CN), State Key Laboratory of Synthetic Chemistry (CN), State Key Laboratory of Synthetical Automation for Process Industries, Universidad del Noreste (MX)
China Association for Science and Technology, National Natural Science Foundation of China, Natural Science Foundation of Hebei Province, Natural Science Foundation of Liaoning Province, Higher Education Discipline Innovation Project, Fundamental Research Funds for the Central Universities, Basic and Applied Basic Research Foundation of Guangdong Province
Openalex Percentile: Top 3%
Tactile and Sensory Interactions
7.16
DOISourceOpenAlex
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