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

International Conference on Physical Instrumentation and Advanced Materials

物理学;材料科学

Triwiyanto^1,3 ; Wahyunggoro, Oyas^3 ; Nugroho, Hanung Adi^3 ; Herianto^2

Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia^1

Department of Mechanical and Industrial Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia^2

Department of Electromedical Engineering, Health Polytechnic of Surabaya, Ministry of Health, Indonesia^3

关键词: Butterworth;    Elbow joints;    Extraction parameters;    Prediction algorithms;    Prediction-based;    Root mean square errors;    Second orders;    Zero-crossing feature;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/853/1/012014/pdf
DOI  :  10.1088/1742-6596/853/1/012014

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

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.