Novel Feature Generation for Multiple Hand Gestures Classification

Abstract
Novel Feature Generation for Multiple Hand Gestures Classification

Abderrazak Chahid 1, Rami Khushaba 2, Adel Al-Jumaily 2 and Taous-Meriem Laleg-Kirati 1

1 King Abdullah University of Science and Technology (KAUST).

2 University of Technology, Sydney (UTS), Australia

Abstract

Surface electromyography (sEMG) signals represent an opportunity to control a multifunctional prosthetic hand in a non-invasive way.

In this work, we investigate a novel feature extraction method that improves the interpretation of sEMG signal of multiple hand gestures.  So, missing body parts could be perfectly restored!!

Introduction

Since prosthesis invention, several prostheses were proposed to replace a missing body part, which may be lost through trauma, diseases, etc. Some of these solutions use the non-invasive sEMG signals to control this device 1,2.

Objective:

  • Build a smart prosthetic hand using artificial intelligence (AI) techniques.

  • Develop a generalizable and robust  AI model for multiple hand gesture’ predictions using a novel feature extraction method.

Challenges:

  • Some hand gesture have similar sEMG signals,

  • Prosthesis response in Real-Time and low cost.

Framework

The proposed framework is described as follows:

  • Quantization:  sEMG signals are converted into sequences using a uniform Quantizer

  • QuPWM features: different features are extracted based on the Position Weight Matrix (PWM) method using multiple patterns (k-mers) 4.

  • Classification: the extracted features are fed to standard classifiers for hand gestures classification.

Conclusion

  • We developed a new feature extraction method using Quantization-based PWM (QuPWM) method.

  • The obtained results are very encouraging and with high accuracy for different subjects.

  • We believe that signal processing is a key to extract the inherent features from biomedical signals such as sEMG,…etc.

  • The proposed features will enhance human–computer interaction (HCI).

Future work

  • Extensive validation using more dataset,

  • Combine these features with deep learning classifier to deal with big data,

  • Integrate the QuPWM in clinical practice: prosthesis.

References

1 Ciancio AL, Cordella F, Hoffmann KP, Schneider A, Guglielmelli E, Zollo L. Current achievements and future directions of hand prostheses controlled via peripheral nervous system. InThe Hand 2017 (pp. 75-95). Springer, Cham.

2 Ahsan MR, Ibrahimy MI, Khalifa OO. Electromygraphy (EMG) signal based hand gesture recognition using artificial neural network (ANN). In2011 4th International Conference on Mechatronics (ICOM) 2011 May 17 (pp. 1-6). IEEE.

3 Du Y, Wenguang J, Wentao W, Geng W. CapgMyo: a high density surface electromyography database for gesture recognition.

4 Chahid A, Albalawi F, Alotaiby TN, Al-Hameed MH, Alshebeili S, Laleg-Kirati TM. QuPWM: Feature Extraction Method for MEG Epileptic Spike Detection. Under revision in IEEE Journal of Biomedical and Health Informatics, arXiv preprint arXiv:1907.02596. 2019 Jul 3.



Conference/Event Name
Digital Health 2020

Additional Links
https://epostersonline.com//dh2020/node/44

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