Name
Abstract | ||
|---|---|---|
The main objective of this study is to recognize sketching precisely and effectively 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 muscles 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 (SVMs). The performance of this model was compared 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 classifier. Our results demonstrate the feasibility of converting forearm sEMG signals into sketching patterns. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s00521-017-2857-3 | Neural Computing and Applications |
Keywords | Field | DocType |
Sketching recognition, Surface electromyography, Support vector machine, Muscle computer interface | Time domain,Pattern recognition,Support vector machine,Curse of dimensionality,Multilayer perceptron,Artificial intelligence,Classifier (linguistics),Artificial neural network,Mathematics,Principal component analysis,Test set | Journal |
Volume | Issue | ISSN |
30 | 9 | 1433-3058 |
Citations | PageRank | References |
3 | 0.40 | 20 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yumiao Chen | 1 | 3 | 1.75 |
| Yang Zhongliang | 2 | 16 | 2.43 |
| Hugh Gong | 3 | 3 | 0.40 |
| Shengze Wang | 4 | 9 | 2.94 |