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Continuous Kalman Estimation Method for Finger Kinematics Tracking from Surface Electromyography. | LitMetric

Continuous Kalman Estimation Method for Finger Kinematics Tracking from Surface Electromyography.

Cyborg Bionic Syst

CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen 518055, China.

Published: May 2024

AI Article Synopsis

  • - The study addresses the challenges of interpreting hand motion intentions using surface electromyography (sEMG), emphasizing the need for continuous kinematics estimation that aligns more naturally with real-life movements rather than just classifying discrete actions.
  • - It proposes a novel continuous Kalman estimation method that uses sEMG and joint angles to accurately infer the motion of fingers, validating its effectiveness with a significant correlation coefficient of 0.73 from a large dataset.
  • - The approach demonstrates impressive computational efficiency, achieving an average processing time of under 0.01 seconds while training on over 45,000 data segments, highlighting its potential for practical applications in finger motion estimation.

Article Abstract

Deciphering hand motion intention from surface electromyography (sEMG) encounters challenges posed by the requisites of multiple degrees of freedom (DOFs) and adaptability. Unlike discrete action classification grounded in pattern recognition, the pursuit of continuous kinematics estimation is appreciated for its inherent naturalness and intuitiveness. However, prevailing estimation techniques contend with accuracy limitations and substantial computational demands. Kalman estimation technology, celebrated for its ease of implementation and real-time adaptability, finds extensive application across diverse domains. This study introduces a continuous Kalman estimation method, leveraging a system model with sEMG and joint angles as inputs and outputs. Facilitated by model parameter training methods, the approach deduces multiple DOF finger kinematics simultaneously. The method's efficacy is validated using a publicly accessible database, yielding a correlation coefficient (CC) of 0.73. With over 45,000 windows for training Kalman model parameters, the average computation time remains under 0.01 s. This pilot study amplifies its potential for further exploration and application within the realm of continuous finger motion estimation technology.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11093877PMC
http://dx.doi.org/10.34133/cbsystems.0094DOI Listing

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