On-Foot Gait Lab: A Hybrid Wearable Sensor Platform for Spatiotemporal Gait Analysis Using Electrospun Piezocapacitive Insoles and IMUs

Flexible electronic insoles offer a promising approach for the assessment of gait-related disorders, such as Parkinson’s Disease (PD), and for the evaluation of plantar pressure and weight distribution. Although recent studies have explored innovative sensing technologies for developing such insoles, versatile and easily implemented solutions suitable for home-based gait monitoring remain limited. Therefore, this paper presents the development of flexible sensorized insoles, integrating electrospun piezocapacitive sensors and an inertial measurement unit (IMU), enabling the accurate collection of spatiotemporal gait parameters and weight-distribution characteristics. The piezocapacitive sensors, comprising a nanofibrous PVAc-graphene dielectric layer, were embedded into commercially available insoles and coupled with a wireless data acquisition system. The system’s performance was validated by measuring stride length, plantar pressure, and weight distribution during simulated PD gait patterns. The results demonstrate high accuracy and precision in measuring stride lengths, and effectively capture peak pressures, weight distributions, and step times. Furthermore, the developed insoles proved versatile for use in outdoor environments and exhibited considerable durability.

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Publication Details

Journal
ACS Applied Engineering Materials
Published
2025-11-03
DOI
https://doi.org/10.1021/acsaenm.5c00834
Citations
2
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
1.35
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article

On-Foot Gait Lab: A Hybrid Wearable Sensor Platform for Spatiotemporal Gait Analysis Using Electrospun Piezocapacitive Insoles and IMUs

Debarun Sengupta, Nataniel dos Santos Gomes, Ajay Giri Prakash Kottapalli, Cole R. Onorati
2 citations
ACS Applied Engineering Materials
Advanced Sensor and Energy Harvesting Materials
1.35
article

On-Foot Gait Lab: A Hybrid Wearable Sensor Platform for Spatiotemporal Gait Analysis Using Electrospun Piezocapacitive Insoles and IMUs

Debarun Sengupta, Nataniel dos Santos Gomes, Ajay Giri Prakash Kottapalli, Cole R. Onorati
article en
2 citations

Abstract

Flexible electronic insoles offer a promising approach for the assessment of gait-related disorders, such as Parkinson’s Disease (PD), and for the evaluation of plantar pressure and weight distribution. Although recent studies have explored innovative sensing technologies for developing such insoles, versatile and easily implemented solutions suitable for home-based gait monitoring remain limited. Therefore, this paper presents the development of flexible sensorized insoles, integrating electrospun piezocapacitive sensors and an inertial measurement unit (IMU), enabling the accurate collection of spatiotemporal gait parameters and weight-distribution characteristics. The piezocapacitive sensors, comprising a nanofibrous PVAc-graphene dielectric layer, were embedded into commercially available insoles and coupled with a wireless data acquisition system. The system’s performance was validated by measuring stride length, plantar pressure, and weight distribution during simulated PD gait patterns. The results demonstrate high accuracy and precision in measuring stride lengths, and effectively capture peak pressures, weight distributions, and step times. Furthermore, the developed insoles proved versatile for use in outdoor environments and exhibited considerable durability.

ACS Applied Engineering MaterialsVol. 4(1)
University of Groningen (NL), Shiv Nadar University (IN), Advanced Materials and Devices (United States) (US), BioElectronics (United States) (US)
Openalex Percentile: Top 17%
Advanced Sensor and Energy Harvesting Materials
1.35
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On-Foot Gait Lab: A Hybrid Wearable Sensor Platform for Spatiotemporal Gait Analysis Using Electrospun Piezocapacitive Insoles and IMUs — Debarun Sengupta, Nataniel dos Santos Gomes, et al. · ACS Applied Engineering Materials (2025) | TGRS Research Map | TGRS