Reliable Vertical Ground Reaction Force Estimation with Smart Insole During Walking
Femi Olugbon, Nozhan Ghoreishi, Ming-Chun Huang, Wenyao Xu, Diliang, Chen

TL;DR
This paper introduces a new machine learning-based method using smart insole data to accurately estimate vertical ground reaction forces during walking, improving reliability over existing wearable solutions.
Contribution
It presents a novel fusion of inertial and center pressed sensor data with advanced machine learning algorithms for robust vGRF estimation.
Findings
Outperforms state-of-the-art methods in accuracy and correlation.
Achieves low error rates in intra- and inter-participant testing.
Demonstrates potential for real-world gait analysis in free-living environments.
Abstract
The vertical ground reaction force (vGRF) and its characteristic weight acceptance and push-off peaks measured during walking are important for gait and biomechanical analysis. Current wearable vGRF estimation methods suffer from drifting errors or low generalization performances, limiting their practical application. This paper proposes a novel method for reliably estimating vGRF and its characteristic peaks using data collected from the smart insole, including inertial measurement unit data and the newly introduced center of the pressed sensor data. These data were fused with machine learning algorithms including artificial neural networks, random forest regression, and bi-directional long-short-term memory. The proposed method outperformed the state-of-the-art methods with the root mean squared error, normalized root mean squared error, and correlation coefficient of 0.024 body…
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Taxonomy
TopicsMuscle activation and electromyography studies · Balance, Gait, and Falls Prevention · Diabetic Foot Ulcer Assessment and Management
