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k-Tournament Grasshopper Extreme Learner for FMG-Based Gesture Recognition.

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A new artificial neural network, the k-tournament grasshopper extreme learner (KTGEL), enhances hand sign recognition using force myography (FMG) signals. This method achieves high accuracy with fewer sensors and a reduced architecture for wearable systems.

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Hand gesture recognition is crucial for various applications.
  • Existing sensor-based systems are complex and require new algorithms for high accuracy and reduced architecture.
  • Force myography (FMG) offers a promising signal basis for hand sign recognition.

Purpose of the Study:

  • To propose a novel classification method for hand sign recognition using an extreme learning machine (ELM) optimized with a grasshopper optimization algorithm (GOA).
  • To develop a k-tournament grasshopper extreme learner (KTGEL) classifier for efficient weight pruning and feature selection.
  • To investigate the impact of sensor count and subject numbers on recognition performance using FMG signals for wearable systems.

Main Methods:

  • A novel KTGEL classifier was developed by integrating an improved GOA for ELM weight pruning.
  • The k-tournament GOA was employed for selecting and pruning ELM weights.
  • FMG signals were utilized, and experiments were conducted to determine the minimal number of sensors and the effect of subject participation.

Main Results:

  • The KTGEL classifier achieved high accuracy (97% microaverage precision and sensitivity) with 3000 hidden nodes in the ELM.
  • KTGEL significantly reduced the number of hidden nodes to 1000 while maintaining comparable sensitivity and only a 1% reduction in precision.
  • Eight sensors were identified as the minimum required for acceptable performance when increasing the number of subjects.

Conclusions:

  • The proposed KTGEL classifier offers an efficient and accurate method for hand sign recognition using FMG signals.
  • KTGEL demonstrates a reduced architecture and automatic feature selection capabilities, suitable for wearable embedded systems.
  • The study provides insights into sensor optimization and subject data requirements for robust hand gesture recognition systems.