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Related Concept Videos

Fast Fourier Transform01:10

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The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
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Discrete Fourier Transform01:15

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The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Human hand gesture recognition using fast Fourier transform with coot optimization based on deep neural network.

Arumugam Arulkumar1, Palanisamy Babu2

  • 1Department of Electrical and Electronics Engineering, Nehru Institute of Engineering and Technology, Coimbatore, India.

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Summary

This study introduces a novel deep neural network (DNN) for accurate hand motion detection in amputees. The method achieves 95% accuracy, improving prosthetic control and quality of life for individuals with limb differences.

Keywords:
EMG signalbutterworth filtercoot optimizationdeep neural networkfast Fourier transformfrequency domain featuressliding windowtime domain features

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Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Rehabilitation Technology

Background:

  • Hand motion detection is crucial for amputees' prosthetic control.
  • Existing algorithms for hand motion detection are often complex and lack accuracy.
  • There is a need for efficient and accurate methods to interpret residual limb signals.

Purpose of the Study:

  • To develop and validate a deep neural network (DNN) model for recognizing human hand movements.
  • To overcome the limitations of existing complex and time-consuming algorithms.
  • To improve the control of prosthetic devices for amputees.

Main Methods:

  • Electromyography (EMG) signals were captured and pre-processed using high-pass Butterworth and low-pass filters.
  • Signals were segmented using a sliding window technique, and features were extracted via Fast Fourier Transform.
  • The Coot optimization algorithm selected optimal features for input into a deep neural network classifier.

Main Results:

  • The proposed DNN model achieved 95% accuracy, 0.05% error rate, 94% precision, and 92% specificity.
  • The developed approach demonstrated superior performance compared to existing methods.
  • The prediction model effectively recognizes human hand movements.

Conclusions:

  • The novel DNN-based approach offers a highly accurate and efficient solution for hand motion detection in amputees.
  • This technology has the potential to significantly improve prosthetic control and the quality of life for individuals with limb amputations.
  • The method provides a promising prediction model for controlling prosthetic limbs based on EMG signals.