Abstract
The main objective of this study is to recognize sketching precisely
and eectively in human computer interaction. A surface electromyography
(sEMG) based sketching recognition method is proposed. We conducted an
experiment in which we recorded the sEMG signals from the forearm mus-
cles of two participants who were instructed to sketch seven basic one-stroke
shapes. Subsequently, seven features of the sEMG time domain were extracted.
After reducing data dimensionality using principal component analysis, these
features were used as input vectors to a sketching recognition model based on
Support Vector Machines (SVM). The performance of this model was com-
pared to two other recognition models based on Multilayer perceptron (MLP)
neural networks and Self Organization Feature Map (SOFM) neural networks.
The average recognition rates for the seven basic one-stroke shapes of two
participants achieved by the SVM-based and MLP-based models were both
98.5% in the test set, which were slightly superior to the performance of the
SOFM classier. Our results demonstrate the feasibility of converting forearm
sEMG signals into sketching patterns.
and eectively in human computer interaction. A surface electromyography
(sEMG) based sketching recognition method is proposed. We conducted an
experiment in which we recorded the sEMG signals from the forearm mus-
cles of two participants who were instructed to sketch seven basic one-stroke
shapes. Subsequently, seven features of the sEMG time domain were extracted.
After reducing data dimensionality using principal component analysis, these
features were used as input vectors to a sketching recognition model based on
Support Vector Machines (SVM). The performance of this model was com-
pared to two other recognition models based on Multilayer perceptron (MLP)
neural networks and Self Organization Feature Map (SOFM) neural networks.
The average recognition rates for the seven basic one-stroke shapes of two
participants achieved by the SVM-based and MLP-based models were both
98.5% in the test set, which were slightly superior to the performance of the
SOFM classier. Our results demonstrate the feasibility of converting forearm
sEMG signals into sketching patterns.
| Original language | English |
|---|---|
| Pages (from-to) | 1-13 |
| Journal | Neural Computing and Applications |
| Early online date | 21 Jan 2017 |
| DOIs | |
| Publication status | Published - 2017 |
