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Finger Motion Decoding Using EMG Signals Corresponding Various Arm Postures
- Issue Date
- 2010
- Publisher
- 한국뇌신경과학회
- Keywords
- surface EMG; finger motions; neural signal processing; HCI; surface EMG; finger motions; neural signal processing; HCI
- Citation
- Experimental Neurobiology, v.19, no.1, pp.54 - 61
- Journal Title
- Experimental Neurobiology
- Volume
- 19
- Number
- 1
- Start Page
- 54
- End Page
- 61
- ISSN
- 1226-2560
- Abstract
- We provide a novel method to infer finger flexing motions using a four‐channel surface electromyogram (EMG). Surface EMG signals can be recorded from the human body non‐invasively and easily. Surface EMG signals in this study were obtained from four channel electrodes placed around the forearm. The motions consist of the flexion of five single fingers (thumb, index finger, middle finger, ring finger, and little finger) and three multi‐finger motions. The maximum likelihood estimation was used to infer the finger motions. Experimental results have shown that this method can successfully infer the finger flexing motions. The average accuracy was as high as 97.75%. In addition, we examined the influence of inference accuracies with the various arm postures.
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Related Researcher
- Shin, Hyun Chool
- College of Information Technology (Department of Electronic Engineering)