The wearable electrocardiogram (W-ECG) signal inherently contains motion artifacts due to various body movements of the wearer. The W-ECG signals with four body movement activities (BMAs)‒ left arm up-down, right arm up-down, waist-twist...
moreThe wearable electrocardiogram (W-ECG) signal inherently contains motion artifacts due to various body movements of the wearer. The W-ECG signals with four body movement activities (BMAs)‒ left arm up-down, right arm up-down, waist-twist and walking have been captured using the wearable ECG recorder. The classification of these four BMAs
has been performed using artificial neural networks (ANN). In the process, the motion artifacts contained in the captured W-ECG signals have been extracted using Wavelet transform and the features of the motion artifacts have been extracted using Gabor transform.
