title: A Wearable Context-Aware ECG Monitoring System Integrated with Built-in Kinematic Sensors of the Smartphone.
abstract: Continuously monitoring the ECG signals over hours combined with activity status is very important for preventing cardiovascular diseases. A traditional ECG holter is often inconvenient to carry because it has many electrodes attached to the chest and because it is heavy. This work proposes a wearable, low power context-aware ECG monitoring system integrated built-in kinetic sensors of the smartphone with a self-designed ECG sensor. The wearable ECG sensor is comprised of a fully integrated analog front-end (AFE), a commercial micro control unit (MCU), a secure digital (SD) card, and a Bluetooth module. The whole sensor is very small with a size of only 58 × 50 × 10 mm for wearable monitoring application due to the AFE design, and the total power dissipation in a full round of ECG acquisition is only 12.5 mW. With the help of built-in kinetic sensors of the smartphone, the proposed system can compute and recognize user's physical activity, and thus provide context-aware information for the continuous ECG monitoring. The experimental results demonstrated the performance of proposed system in improving diagnosis accuracy for arrhythmias and identifying the most common abnormal ECG patterns in different activities. In conclusion, we provide a wearable, accurate and energy-efficient system for long-term and context-aware ECG monitoring without any extra cost on kinetic sensor design but with the help of the widespread smartphone.
authors.affiliation: Key Laboratory for Health Informatics of the Chinese Academy of Sciences (HICAS), Shenzhen Institutes of Advanced Technology, 1068 Xueyuan Boulevard, Shenzhen 518055, China. fen.miao@siat.ac.cn.|Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China. fen.miao@siat.ac.cn.
authors.forename: Fen
authors.initials: F
authors.lastname: Miao
authors.affiliation: Key Laboratory for Health Informatics of the Chinese Academy of Sciences (HICAS), Shenzhen Institutes of Advanced Technology, 1068 Xueyuan Boulevard, Shenzhen 518055, China. yy.cheng@blestech.com.
authors.forename: Yayu
authors.initials: Y
authors.lastname: Cheng
authors.affiliation: High-field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, Tuebingen 72076, Germany. yi.he@tuebingen.mpg.de.
authors.forename: Yi
authors.initials: Y
authors.lastname: He
authors.affiliation: Key Laboratory for Health Informatics of the Chinese Academy of Sciences (HICAS), Shenzhen Institutes of Advanced Technology, 1068 Xueyuan Boulevard, Shenzhen 518055, China. qy.he@siat.ac.cn.|Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China. qy.he@siat.ac.cn.
authors.forename: Qingyun
authors.initials: Q
authors.lastname: He
authors.affiliation: Key Laboratory for Health Informatics of the Chinese Academy of Sciences (HICAS), Shenzhen Institutes of Advanced Technology, 1068 Xueyuan Boulevard, Shenzhen 518055, China. ye.li@siat.ac.cn.
authors.forename: Ye
authors.initials: Y
authors.lastname: Li
mesh_terms: D061725:Accelerometry / Q000295:instrumentation*; D000203:Activities of Daily Living; D000293:Adolescent; D000328:Adult; D001696:Biomechanical Phenomena; D040421:Cell Phone; D015716:Electrocardiography, Ambulatory / Q000295:instrumentation*; D005260:Female; D006801:Humans; D008297:Male; D063731:Mobile Applications; D055815:Young Adult
keywords: context-aware; physical activity recognition; power control; wearable ECG sensor