Enhancing the Reliability of Wearable Cardiac Monitoring using Accelerometer Activity Data

We developed a system for monitoring both activity and electrocardiogram for improved reliability of cardiac monitoring. Additionally, the link between activity information and recorded cardiac information can be used to better incorporate physiological state in analysis in free-living conditions. Our approach uses a machine learning model to predict the activity based on accelerometer data that is subsequently used to estimate cardiac monitoring reliability and linking the heart rate data in to a physical activity. We collected proof-of-concept data from eight healthy volunteers using accelerometers on wrist and on thigh and an electrocardiogram (ECG). The measurement protocol included eight activities (lying, sitting, standing, walking, jogging, walking stairs up and down and cycling). Each measurement was one minute long and the set was repeated 5-10 times per research participant. In addition, three individuals conducted outdoor "free-living" measurements which were used for testing. Time- and frequency-domain features were extracted and XGBoost model was trained. The model was able to recognize correct activity with a mean accuracy of 84 % and 87 % with leave-one-subject-out and leave-half-subject-out cross validation, respectively. The MA-AUC was 0.97 for both cross validations. From the "free-living" test measurements the activities were correctly predicted with a mean accuracy of 81 %. Quality of the ECG varied between activities and was the highest for lying and the lowest for jogging and the quality metric had a strong correlation with heart rate estimation error.