Physical activities recognition from ambulatory ECG signals using neuro-fuzzy classifiers and support vector machines

Abstract

The use of wearable recorders for long-term monitoring of physiological parameters has increased in the last few years. The ambulatory electrocardiogram (A-ECG) signals of five healthy subjects with four body movements or physical activities (PA)—left arm up down, right arm up down, waist twisting and walking—have been recorded using a wearable ECG recorder. The classification of these four PAs has been performed using neuro-fuzzy classifier (NFC) and support vector machines (SVM). The PA classification is based on the distinct, time-frequency features of the extracted motion artifacts contained in recorded A-ECG signals. The motion artifacts in A-ECG signals have been separated first by the discrete wavelet transform (DWT) and the time–frequency features of these motion artifacts have then been extracted using the Gabor transform. The Gabor energy feature vectors have been fed to the NFC and SVM classifiers. Both the classifiers have achieved a PA classification accuracy of over 95% for all subjects.

• A-ECG signal
• neuro-fuzzy classifiers
• physical activities
• support vector machines
• wavelet transform

Acknowledgement

The authors would like to thank the Charutar Vidyamandal (CVM), Vallabh Vidyanagar, India and Sophisticated Instrumentation Centre for Advanced Research and Testing (SICART), Vallabh Vidyanagar, India for their support during this work.

Declaration of interest

The authors report no conflict of interest. The authors alone are responsible for the content and writing of this article.