Extracting time-frequency feature of single-channel vastus medialis EMG signals for knee exercise pattern recognition

Abstract

The EMG signal indicates the electrophysiological response to daily living of activities, particularly to lower-limb knee exercises. Literature reports have shown numerous benefits of the Wavelet analysis in EMG feature extraction for pattern recognition. However, its application to typical knee exercises when using only a single EMG channel is limited. In this study, three types of knee exercises, i.e., flexion of the leg up (standing), hip extension from a sitting position (sitting) and gait (walking) are investigated from 14 healthy untrained subjects, while EMG signals from the muscle group of vastus medialis and the goniometer on the knee joint of the detected leg are synchronously monitored and recorded. Four types of lower-limb motions including standing, sitting, stance phase of walking, and swing phase of walking, are segmented. The Wavelet Transform (WT) based Singular Value Decomposition (SVD) approach is proposed for the classification of four lower-limb motions using a single-channel EMG signal from the muscle group of vastus medialis. Based on lower-limb motions from all subjects, the combination of five-level wavelet decomposition and SVD is used to comprise the feature vector. The Support Vector Machine (SVM) is then configured to build a multiple-subject classifier for which the subject independent accuracy will be given across all subjects for the classification of four types of lower-limb motions. In order to effectively indicate the classification performance, EMG features from time-domain (e.g., Mean Absolute Value (MAV), Root-Mean-Square (RMS), integrated EMG (iEMG), Zero Crossing (ZC)) and frequency-domain (e.g., Mean Frequency (MNF) and Median Frequency (MDF)) are also used to classify lower-limb motions. The five-fold cross validation is performed and it repeats fifty times in order to acquire the robust subject independent accuracy. Results show that the proposed WT-based SVD approach has the classification accuracy of 91.85%±0.88% which outperforms other feature models.

Evaluation of Knee Activities Using EMG Signals for Pre-predicting Lower Limb Dystonia Diseases

“…In this scenario, for experimental simulation, MATLAB (version 2017a) was employed on PC with 3.2 GHz with i5 processor. In order to estimate the efficiency of proposed algorithm, the performance of proposed method was compared with Linear discriminant analysis (LDA) [18] and Support vector regression [19] on the reputed database EMG-Lower Limb dataset. The performance of the proposed method was compared in terms of accuracy, precision, recall, sensitivity, specificity and E-rate.…”

“…In addition, Y. Zhang, P. Li, X. Zhu, S.W. Su, Q. Guo, P. Xu, and D. Yao, [19] developed an effective system for knee pattern identification. In first phase, a combination of both time and frequency domain features were utilized to extract the features from EMG-Lower Limb dataset.…”

Daily Activity Monitoring and Fall Detection Based on Surface Electromyography and Plantar Pressure

“…In case of an accident, if an elderly cannot be found immediately and rescued in time, then serious consequences may occur. Hence, studying daily activity monitoring and fall detection is critical to reducing health and nancial burdens [4]. In addition, daily activity recognition is bene cial to rehabilitation engineering [5].…”

Sensor fusion using EMG and vision for hand gesture classification in mobile applications

“…Power Spectrum Density, Fast Fourier Transform) [8] or in time-frequency (e.g. Wavelet Transform) [9] domains. More recently, a new approach for classifying EMG signals started to emerge based on the use of Spiking Neural Networks (SNNs) on neuromorphic chips [10].…”