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Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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Improving Long Term Myoelectric Decoding, Using an Adaptive Classifier with Label Correction.

Sarthak Jain1, Girish Singhal1, Ryan J Smith2

  • 1Department of Electrical Engineering, Indian Institute of Technology Gandhinagar, Gujarat India 382424.

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Summary
This summary is machine-generated.

This study introduces an adaptive myoelectric decoding algorithm that significantly reduces accuracy decay in upper limb prosthesis control. The novel approach maintains long-term decoding accuracy in electromyography (EMG) signals for improved prosthesis performance.

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Robotics
  • Neuroprosthetics

Background:

  • Myoelectric decoding algorithms for prosthesis control suffer from accuracy degradation over time due to muscle signal variations.
  • Existing algorithms struggle to adapt to both gradual and rapid changes in electromyography (EMG) signals.

Purpose of the Study:

  • To develop a novel adaptive myoelectric decoding algorithm for upper limb prosthesis control.
  • To address and mitigate the inherent decay in decoding accuracy observed in current myoelectric systems.

Main Methods:

  • The proposed algorithm utilizes unsupervised, on-demand updates of the training set to adapt to signal changes.
  • It incorporates training data updates for slow signal variations and label correction for fast signal variations.
  • Myoelectric data was collected from an able-bodied user over 4.5 hours during eight distinct wrist movements.

Main Results:

  • The adaptive algorithm demonstrated a significantly lower decay rate of 0.2 per hour compared to 3.3 per hour for a non-adaptive classifier.
  • This indicates a substantial improvement in maintaining decoding accuracy over extended periods.

Conclusions:

  • The developed adaptive algorithm effectively maintains long-term decoding accuracy in EMG signals.
  • This advancement promises to enhance the overall performance and reliability of myoelectric upper limb prostheses.