Effect of window length on performance of the elbow-joint angle prediction based on electromyography

• Published in: Journal of physics. Conference series Vol. 853; no. 1; pp. 12014 - 12022
• Main Authors: Triwiyanto, Wahyunggoro, Oyas, Nugroho, Hanung Adi, Herianto
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.05.2017
• Subjects: Algorithms; Elbow (anatomy); Electromyography; Feature extraction; Joints (anatomy); Low pass filters; Muscles; Performance prediction; Physics
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/853/1/012014

The high performance of the elbow joint angle prediction is essential on the development of the devices based on electromyography (EMG) control. The performance of the prediction depends on the feature of extraction parameters such as window length. In this paper, we evaluated the effect of the window length on the performance of the elbow-joint angle prediction. The prediction algorithm consists of zero-crossing feature extraction and second order of Butterworth low pass filter. The feature was used to extract the EMG signal by varying window length. The EMG signal was collected from the biceps muscle while the elbow was moved in the flexion and extension motion. The subject performed the elbow motion by holding a 1-kg load and moved the elbow in different periods (12 seconds, 8 seconds and 6 seconds). The results indicated that the window length affected the performance of the prediction. The 250 window lengths yielded the best performance of the prediction algorithm of (mean±SD) root mean square error = 5.68%±1.53% and Person's correlation = 0.99±0.0059.