Energy Efficient Framework for a AIoT Cardiac Arrhythmia Detection System Wearable during Sport

Applied Sciences

卷:12

Andrea C. Castillo-Atoche¹  Javier Vázquez-Castillo²  Ramón Atoche-Enseñat³  Adolfo Espinoza-Ruiz⁴  Johan J. Estrada-López⁵  Orlando Palma-Marrufo⁶  Alejandro Castillo-Atoche⁶  Karim Caamal-Herrera⁶ 

[1] Chemistry and Biochemistry Department, Tecnológico Nacional de México/Instituto Tecnológico de Mérida, Mérida 97118, Mexico;

[2] Department of Electrical Engineering, University of Quintana Roo, Chetumal 77019, Mexico;

[3] Electronics Department, Tecnológico Nacional de México/Instituto Tecnológico de Mérida, Mérida 97118, Mexico;

[4] Electronics and Electrical Engineering Department, Technological Institute of Sonora, Ciudad Obregón 85000, Mexico;

[5] Faculty of Mathematics, Autonomous University of Yucatan, Mérida 97000, Mexico;

[6] Mechatronics Department, Autonomous University of Yucatan, Mérida 97000, Mexico;

关键词: convolutional neural network;    dynamic power management;    energy harvesting;    artificial intelligence-of-things;    sport wearable;   

DOI  :  10.3390/app12052716

来源: DOAJ

【 摘 要 】

The growing market of wearables is expanding into different areas of application such as devices designed to improve and monitor sport activities. This in turn is pushing research on low-cost, very low-power wearable systems with increased analysis capabilities. This paper proposes integrated energy-aware techniques and a convolutional neural network (CNN) for a cardiac arrhythmia detection system that can be worn during sport training sessions. The dynamic power management strategy (DPMS) is programmed into an ultra-low-power microcontroller, and in combination with a photovoltaic (PV) energy harvesting (EH) circuit, achieves a battery-life extension towards a self-powered operation. The CNN-based analysis filters, scales the image, and using a bicubic technique, interpolates the measurements to subsequently classify the electrocardiogram (ECG) signal into normal and abnormal patterns. Experimental results show that the EH-DPMS achieves an extension in the battery charge for a total of 14.34% more energy available, which represents 12 consecutive workouts of 45 min without the need to manually recharge it. Furthermore, an arrhythmia detection precision of 98.6% is achieved among the experimental sessions using 55,222 images for training the system with the MIT-BIH, QT, and long-term ST databases, and 1320 implemented on a wearable system. Therefore, the proposed wearable system can be used to monitor an athlete’s condition, reducing the risk of abnormal heart conditions during sports activities.

【 授权许可】

Unknown