Joint angle estimation with wavelet neural networks

This paper presents a wavelet neural network (WNN) based method to reduce reliance on wearable kinematic sensors in gait analysis. Wearable kinematic sensors hinder real-time outdoor gait monitoring applications due to drawbacks caused by multiple sensor placements and sensor offset errors. The prop...

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Bibliographic Details
Published in:Scientific reports 2021-05, Vol.11 (1), p.10306-10306, Article 10306
Main Authors: Sivakumar, Saaveethya, Gopalai, Alpha Agape, Lim, King Hann, Gouwanda, Darwin, Chauhan, Sunita
Format: Article
Language:English
Subjects:
631/114/1305
639/166/985
Ankle
Gait
Humanities and Social Sciences
Kinematics
multidisciplinary
Neural networks
Science
Science (multidisciplinary)
Sensors
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Summary:This paper presents a wavelet neural network (WNN) based method to reduce reliance on wearable kinematic sensors in gait analysis. Wearable kinematic sensors hinder real-time outdoor gait monitoring applications due to drawbacks caused by multiple sensor placements and sensor offset errors. The proposed WNN method uses vertical Ground Reaction Forces (vGRFs) measured from foot kinetic sensors as inputs to estimate ankle, knee, and hip joint angles. Salient vGRF inputs are extracted from primary gait event intervals. These selected gait inputs facilitate future integration with smart insoles for real-time outdoor gait studies. The proposed concept potentially reduces the number of body-mounted kinematics sensors used in gait analysis applications, hence leading to a simplified sensor placement and control circuitry without deteriorating the overall performance.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-021-89580-y