Auto-Segmentation Analysis of EMG Signal for Lifting Muscle Contraction Activities

Time-frequency representation of a signal has been widely used in various research areas to analyze non-stationary signals (ie. electromyography (EMG) signals). However, due to the high computational complexity of certain time-frequency distribution techniques, the application of these techniques in the analysis of long duration EMG signals is not suitable. To overcome this problem, muscle contraction segmentation is essential to process the existed EMG signals, since not all of the EMG signal contains valid information to be analyzed. Thus, this paper presents an algorithm to automatically detect and segment the muscle contractions existed in EMG signal during long duration recordings. Surface EMG signals were collected from biceps branchii muscle of ten subjects during manual lifting. Subjects were required to lift a 5 kg load mass with lifting height of 75 cm until experiencing fatigue. The utilization of instantaneous energy of EMG is used to estimate the presence of first muscle contraction, second muscle contraction and until the last muscle contraction. This instantaneous energy is obtained from spectrogram and a threshold value is set to differentiate between muscle contractions and noise. This research shows that the algorithm is able to automatically segment muscle contractions in EMG signal based on the signal instantaneous energy

Classification of Myoelectric Signal using Spectrogram Based Window Selection

This paper presents a study of the classification of myoelectric signal using spectrogram with different window sizes. The electromyography (EMG) signals of 40 hand movement types are collected from 10 subjects through NinaPro database. By employing spectrogram, the EMG signals are represented in time-frequency representation.  Ten features are extracted from spectrogram for performance evaluation. In this study, two classifiers namely support vector machine (SVM) and linear discriminate analysis (LDA) are used to evaluate the performance of spectrogram features in the classification of EMG signals. To determine the best window size in spectrogram, three different Hanning window sizes are examined. The experimental results indicate that by applying spectrogram with optimize window size and LDA, the highest mean classification accuracy of 91.29% is obtained

Electromyography Signal Analysis Using Time and Frequency Domain for Health Screening System Task

Musculoskeletal disorder (MSDs) isone of the most popular issues of occupationalinjuries and disabilities. It has a big impact andcreates a big problem for industries to be resolved.In MSDs, electromyography (EMG) is one of themethods to be studied in order to detect MSDsproblem. This research focuses on the EMG signalanalysis by using time domain and frequencydomain (Welch Power Spectral Density) method.It gives more information from the signal and itis the most suitable method for classifying themoments in order to identify the behaviouralof the signals. Axial rotational reach and upperlevel reach task from Health Screening Program(HST) is performed using functional range ofmotion (FROM) by considering left and rightbiceps brachii muscles to be analysed. There aretwo parameters chosen for each time and for eachfrequency domain to be tested, which are meanan absolute value (MAV) and root mean square(RMS) for time domain. Median frequency (MDF)and mean frequency (MNF) are for frequencydomain. The results showed that frequencydomain analysis is able to give more parameterand information of the signal. Upper level reachacquires more effort to perform the task comparedto axial rotational reach for left and right bicepsbrachii. However, different performances ofthe signal obtained in classifying the momentsfrom t-test analysis due to p-value. The bestperformance to classify signal characteristics is thelowest p-value which is 7.369E-05 (MAV), 6.9504E-05 (RMS), 0.0054 (MDF). However, p-value for0.0515 is rejected because it is greater than 0.05.It is concluded that the frequency domain is ableto give more information of the signal, howeverfor classifications moments, time domain is bettercompared to the higher accuracy result. This studyis very important to give the idea in the futureanalysis of EMG signal in the aspect of detectingMSDs in human body in health screening task