Step Estimator Based on a Wearable ECG Monitor

Abstract

ECG signal acquisition may be contaminated with various unwanted signals from the environment, but the contaminating signals are not always be categorized as noise. In this paper, we use the signal processing method to extract the step signal from an ECG signal. The proposed algorithm has been designed, tested, and evaluated using ensemble empirical mode decomposition (EEMD) algorithm as ECG data extraction and accelerometer data as the step counter reference. The main idea is to choose the correct intrinsic mode function (IMF) that represents gait signal delineation of ECG, and then calculating the detection accuracy of gait signal based on the step counter from accelerometer data. We use different belt tightness configurations based on chest circumference for each subject during the treadmill test, and then perform the detection accuracy for each configuration. Five healthy subjects were participated in the treadmill test as training subjects. Each participant was asked to walk in four levels of speeds and continue to run in five different speeds: 1.8, 2.7, 3.6, 4.5, 5.4, 6.3, 7.2, 8.1, and 9.0 km/h. Later on, other five healthy subjects were used to evaluate the accuracy of the proposed algorithm using identical configuration as training subjects. The proposed algorithm is able to achieve the detection accuracy of 91.93% and 94.72% for training and testing subjects using the belt tightness scenario of 100% length of chest circumference.