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 language | English (US) |
|---|---|
| Article number | 7320970 |
| Pages (from-to) | 1687-1698 |
| Number of pages | 12 |
| Journal | IEEE Transactions on Biomedical Engineering |
| Volume | 63 |
| Issue number | 8 |
| DOIs | |
| State | Published - Aug 1 2016 |
| Externally published | Yes |
Fingerprint
Keywords
- Image motion analysis
- pattern classification
- prosthetic control
- rehabilitation
- ultrasound imaging
ASJC Scopus subject areas
- Biomedical Engineering
Cite this
Real-time classification of hand motions using ultrasound imaging of forearm muscles. / Akhlaghi, Nima; Baker, Clayton A.; Lahlou, Mohamed; Zafar, Hozaifah; Murthy, Karthik G.; Rangwala, Huzefa S.; Kosecka, Jana; Joiner, Wilsaan; Pancrazio, Joseph J.; Sikdar, Siddhartha.
In: IEEE Transactions on Biomedical Engineering, Vol. 63, No. 8, 7320970, 01.08.2016, p. 1687-1698.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Real-time classification of hand motions using ultrasound imaging of forearm muscles
AU - Akhlaghi, Nima
AU - Baker, Clayton A.
AU - Lahlou, Mohamed
AU - Zafar, Hozaifah
AU - Murthy, Karthik G.
AU - Rangwala, Huzefa S.
AU - Kosecka, Jana
AU - Joiner, Wilsaan
AU - Pancrazio, Joseph J.
AU - Sikdar, Siddhartha
PY - 2016/8/1
Y1 - 2016/8/1
N2 - 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.
AB - 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.
KW - Image motion analysis
KW - pattern classification
KW - prosthetic control
KW - rehabilitation
KW - ultrasound imaging
UR - http://www.scopus.com/inward/record.url?scp=84986242737&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84986242737&partnerID=8YFLogxK
U2 - 10.1109/TBME.2015.2498124
DO - 10.1109/TBME.2015.2498124
M3 - Article
C2 - 26560865
AN - SCOPUS:84986242737
VL - 63
SP - 1687
EP - 1698
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
SN - 0018-9294
IS - 8
M1 - 7320970
ER -