Activity Recognition in Wearable ECG Monitoring Aided by Accelerometer Data

A wearable ECG monitoring device with accelerometer aided activity recognition is proposed in this work. A 3-axis accelerometer is integrated in the Band-Aid alike wearable device which will be stuck to the user's chest. The Lead V2 ECG signal and the chest acceleration data are recorded synchronously. An activity recognition algorithm is proposed to identify certain types of daily activities, including coughing, walking, standing, sitting, squatting or lying based on the chest acceleration data. The recognition result can be further used to correlate the recorded ECG signal to the user's activity. Experiments on 13 volunteers with age of 5 to 68 show that the proposed algorithm have an overall accuracy of 96.92%. The recognition result can be further used to correlate the recorded ECG signal to the user's activities.