Elastomeric Fiber Optic-Embedded Gait Monitoring Insole

With the increasing demand for daily gait monitoring devices, this study presents an intelligent insole equipped with an array of elastomeric optical fibers that serves as an affordable, flexible, and accurate foot pressure sensor. As the weight-bearing foot compresses the soft optical fibers connected to light sources, the amount of transmitted light decreases proportionally to the load. Fiber optic-embedded insole prototyped using 3D printed mold and polyurethane cast foam in various foot sizes. During walking, the raw signal from our optimized prototypes showed a satisfactory correlation with the signal trend of a clinical-grade commercial sensor (r 2 mean = 0.84). The correlation was significantly improved (r 2 mean = 0.945) by implementing a simple machine learning model. The capabilities of the sensor extended to predicting the center of force of plantar pressure and tracking the toe-walking pattern, one of the most common abnormal gaits in children with autism spectrum disorder.

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Publication Details

Journal
Clothing and Textiles Research Journal
Published
2025-09-15
DOI
https://doi.org/10.1177/0887302x251376673
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
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article

Elastomeric Fiber Optic-Embedded Gait Monitoring Insole

Clothing and Textiles Research Journal
Geochemistry and Geologic Mapping
article

Elastomeric Fiber Optic-Embedded Gait Monitoring Insole

article en

Abstract

With the increasing demand for daily gait monitoring devices, this study presents an intelligent insole equipped with an array of elastomeric optical fibers that serves as an affordable, flexible, and accurate foot pressure sensor. As the weight-bearing foot compresses the soft optical fibers connected to light sources, the amount of transmitted light decreases proportionally to the load. Fiber optic-embedded insole prototyped using 3D printed mold and polyurethane cast foam in various foot sizes. During walking, the raw signal from our optimized prototypes showed a satisfactory correlation with the signal trend of a clinical-grade commercial sensor (r 2 mean = 0.84). The correlation was significantly improved (r 2 mean = 0.945) by implementing a simple machine learning model. The capabilities of the sensor extended to predicting the center of force of plantar pressure and tracking the toe-walking pattern, one of the most common abnormal gaits in children with autism spectrum disorder.

Clothing and Textiles Research Journal
University of Georgia (US), Cornell University (US)
Openalex Percentile: Top 40%
Geochemistry and Geologic Mapping
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