Finger movements recognition using minimally redundant features of wavelet denoised EMG

Developing prosthetic hands with high functionality and ease of use is the focus of current research in the area of Electromyogram (EMG) based prosthesis control. Although individuals with upper limb loss can perform grasping operations with currently available prosthetic hands, more intuitive contr...

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Bibliographic Details
Published in:Health and technology 2019-08, Vol.9 (4), p.579-593
Main Authors: Phukan, Nabasmita, Kakoty, Nayan M., Shivam, Prastuti, Gan, John Q.
Format: Article
Language:English
Subjects:
Amputation
Biological and Medical Physics
Biomedical Engineering and Bioengineering
Biomedicine
Biophysics
Classification
Computational Biology/Bioinformatics
Electrodes
Electromyography
Engineering
Fingers
Fourier transforms
Hand (anatomy)
Human motion
Medicine/Public Health
Noise reduction
Original Paper
Peak frequency
Prostheses
R & D/Technology Policy
Redundancy
Support vector machines
Wavelet transforms
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Summary:Developing prosthetic hands with high functionality and ease of use is the focus of current research in the area of Electromyogram (EMG) based prosthesis control. Although individuals with upper limb loss can perform grasping operations with currently available prosthetic hands, more intuitive control of finger movements is required to replicate the complex motor functions of human hands. A significant challenge is to classify the finger movements with higher recognition rates using a smaller number of EMG channels. This paper reports a novel criterion for selection of minimally redundant EMG feature set for classification of 10-class finger movements using two-channel EMG. The feature set is selected from wavelet denoised EMG at four decomposition levels using minimum redundancy in terms of mutual information. A set of five features: root mean square, simple square integral, slope sign change, peak frequency and power spectral ratio have been selected from 31 time and frequency domain features. Using the current state of the art classification technique based on support vector machine, we achieved 10-fold cross-validation recognition rate of 96.5 ± 0.13%. The experimental study shows that the feature set with minimum redundancy in terms of mutual information ensures the highest recognition rate with reduced computational cost.
ISSN:2190-7188
2190-7196
DOI:10.1007/s12553-019-00338-z