Joint angle estimation with wavelet neural networks

Saaveethya Sivakumar, Alpha Agape Gopalai, King Hann Lim, Darwin Gouwanda, Sunita Chauhan

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number10306
Number of pages15
JournalScientific Reports
Volume11
Issue number1
DOIs
Publication statusPublished - 13 May 2021

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