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A Wavelet-Based Approach for Estimating the Joint Angles of the Fingers and Wrist Using Electromyography Signals

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New Technologies to Improve Patient Rehabilitation (REHAB 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1002))

Abstract

The estimation of the joint angles of the fingers and the wrist using electromyography (EMG) signals is essential to enhance the quality of life for amputees, but this task is often considered challenging. In fact, developing robust mechanisms that can estimate the values of the joint angles in the hand provides better control of prosthetic hands and enables the execution of various daily life activities. In this chapter, we present an EMG-based approach for estimating the joint angles of the fingers and wrist. The proposed approach utilizes the discrete wavelet transform (DWT) to analyze the EMG signals in the time-frequency domain. Then, we extract a set of features based on the obtained detail and approximation coefficients of the DWT. The extracted features are used to train a set of support vector regression (SVR) models to estimate the joint angles of the fingers and wrist. To evaluate the performance of the proposed approach, we employed the publicly available NinaPro database, namely database 1, which comprises the EMG signals along with the hand kinematic data recorded for 27 healthy subjects while performing 52 hand movements. The results presented in this chapter demonstrate the capability of the proposed approach to estimate the joint angles of the fingers and wrist.

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Acknowledgments

This research was supported by the Scientific Research Support Fund - Jordan (grant no. ENG/1/9/2015). Also, this research was partially supported by the Seed-Grant program at the German Jordanian University (grant no. SAMS 8/2014).

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Authors and Affiliations

  1. School of Electrical Engineering and Information Technology, German Jordanian University, Amman, 11180, Jordan

    Rami Alazrai, Mohammad I. Daoud & Ala Khalifeh

  2. School of Applied Medical Sciences, German Jordanian University, Amman, 11180, Jordan

    Nasim Alnuman

  3. School of Engineering and Applied Sciences, Western Kentucky University, Bowling Green, KY, 42101, USA

    Yaser Mowafi

  4. School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Deena Alabed

Authors
  1. Rami Alazrai
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  2. Mohammad I. Daoud
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  3. Ala Khalifeh
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  4. Nasim Alnuman
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  5. Yaser Mowafi
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  6. Deena Alabed
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Corresponding author

Correspondence to Rami Alazrai .

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Editors and Affiliations

  1. King Abdulaziz University, Jeddah, Saudi Arabia

    Habib M. Fardoun

  2. Center for Accreditation and Quality, Ahlia University, Manama, Bahrain

    Ahlam A. M. Hassan

  3. Computer Science Research Institute, University of Castilla-La Mancha, Albacete, Spain

    M. Elena de la Guía

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Alazrai, R., Daoud, M.I., Khalifeh, A., Alnuman, N., Mowafi, Y., Alabed, D. (2019). A Wavelet-Based Approach for Estimating the Joint Angles of the Fingers and Wrist Using Electromyography Signals. In: Fardoun, H., Hassan, A., de la Guía, M. (eds) New Technologies to Improve Patient Rehabilitation. REHAB 2016. Communications in Computer and Information Science, vol 1002. Springer, Cham. https://doi.org/10.1007/978-3-030-16785-1_3

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  • DOI: https://doi.org/10.1007/978-3-030-16785-1_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-16784-4

  • Online ISBN: 978-3-030-16785-1

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