Finger Movement Regression with Myoelectric Signal and Deep Neural Network
Date
2019-10-16Author
Anam, Khairul
Swasono, Dwiretno Istiyadi
Muttaqin, Aris Zainul
Hanggara, Faruq Sandi
Metadata
Show full item recordAbstract
Research on electromyographic (EMG) signals is
intensively carried out to help disabled people to control
prosthetic hands. Neural Networks have been widely used in
research on the classification of finger movements using EMG.
The study of a classification system generally still works on a
limited number of movements, even though the human body,
especially fingers, has a nearly unlimited combination of
movements to help do daily activities. To overcome this, a
proportional control system is needed. In its recent
development, research on myoelectric control using EMG
devices is still in a laboratory environment. Hence, the results
obtained in a clinical setting are often different. However, along
with technological developments, the emergence of affordable
and wearable commercial EMG devices such as Myo Armband,
has encouraged this study to develop control systems of
prosthetic fingers using regression. One of many options
available is neural networks that have been widely used in
various fields. By estimating each joint with a different neural
network, the result shows the predicted is fitted to the actual angle with R2 high as 99%.
Collections
- LSP-Conference Proceeding [1873]