Real-time classification of hand motions using ultrasound imaging of forearm muscles

Nima Akhlaghi, Clayton A. Baker, Mohamed Lahlou, Hozaifah Zafar, Karthik G. Murthy, Huzefa S. Rangwala, Jana Kosecka, Wilsaan Joiner, Joseph J. Pancrazio, Siddhartha Sikdar

Research output: Contribution to journalArticle

44 Scopus citations

Abstract

Surface electromyography (sEMG) has been the predominant method for sensing electrical activity for a number of applications involving muscle-computer interfaces, including myoelectric control of prostheses and rehabilitation robots. Ultrasound imaging for sensing mechanical deformation of functional muscle compartments can overcome several limitations of sEMG, including the inability to differentiate between deep contiguous muscle compartments, low signal-to-noise ratio, and lack of a robust graded signal. The objective of this study was to evaluate the feasibility of real-time graded control using a computationally efficient method to differentiate between complex hand motions based on ultrasound imaging of forearm muscles. Dynamic ultrasound images of the forearm muscles were obtained from six able-bodied volunteers and analyzed to map muscle activity based on the deformation of the contracting muscles during different hand motions. Each participant performed 15 different hand motions, including digit flexion, different grips (i.e., power grasp and pinch grip), and grips in combination with wrist pronation. During the training phase, we generated a database of activity patterns corresponding to different hand motions for each participant. During the testing phase, novel activity patterns were classified using a nearest neighbor classification algorithm based on that database. The average classification accuracy was 91%. Real-time image-based control of a virtual hand showed an average classification accuracy of 92%. Our results demonstrate the feasibility of using ultrasound imaging as a robust muscle-computer interface. Potential clinical applications include control of multiarticulated prosthetic hands, stroke rehabilitation, and fundamental investigations of motor control and biomechanics.

Original languageEnglish (US)
Article number7320970
Pages (from-to)1687-1698
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Volume63
Issue number8
DOIs
StatePublished - Aug 1 2016
Externally publishedYes

Keywords

  • Image motion analysis
  • pattern classification
  • prosthetic control
  • rehabilitation
  • ultrasound imaging

ASJC Scopus subject areas

  • Biomedical Engineering

Fingerprint Dive into the research topics of 'Real-time classification of hand motions using ultrasound imaging of forearm muscles'. Together they form a unique fingerprint.

  • Cite this

    Akhlaghi, N., Baker, C. A., Lahlou, M., Zafar, H., Murthy, K. G., Rangwala, H. S., Kosecka, J., Joiner, W., Pancrazio, J. J., & Sikdar, S. (2016). Real-time classification of hand motions using ultrasound imaging of forearm muscles. IEEE Transactions on Biomedical Engineering, 63(8), 1687-1698. [7320970]. https://doi.org/10.1109/TBME.2015.2498124