No abstract

The aim of this work is to assess the muscle fatigue condition using multimodal system. Muscle fatigue is a common muscle condition which experiences in our daily activity. There were 20 subjects participated in this study. Electromyogram (EMG) (shows the electrical activity of the muscle), Mechanomyogram (MMG) (shows a mechanical activity of the muscle) and Acoustic myogram (AMG) (is audible produced when the muscle was contracted) were used in this study. EMG, MMG and AMG were recorded continuously from hamstring muscle, according to the data acquisition protocol. The recorded signals were segmented into fatigue and non-fatigue. Time domain, frequency domain and time-frequency domain features were extracted from the myograms. The extracted features were classified using k-nearest neighbor. The mean accuracy of EMG, MMG and AMG was 87.10%, 81.40% and 67.23% respectively. The mean accuracy of the multimodal system was 92.07%. In this paper, we also have discussed the effect of single myogram and multi modal myograms.

Electroencephalogram (EEG) is used to measure the bioelectric potential on the brain scalp. The recorded EEG signal can have different types of artifacts and the interpretation of a noisy EEG signal is difficult. In this research work, a simple method is proposed to minimize the artifacts present in the EEG signals recorded while perceiving a pure tone. The recorded EEG signal can contain artifacts, such as movement artifacts, muscle contraction artifacts and saturation artifacts. In the proposed method, fractal dimension based features with different interval length and time-domain based energy features were extracted from the EEG signals with and without simulated noise. Using the extracted features, neural network models were developed to classify the EEG signal as a normal or a noisy signal. Further, the performance of the model is also evaluated in terms of classification rate. From the results, it is observed that the neural network model developed with the combined fractal dimension features of interval length 2,3,4,5 and 6 with frame size 128 has the highest classification accuracy of 95.5%.

No abstract

No abstract