The prime purpose of this project is to develop a portable cardiac abnormality monitoring device which can drastically improvise the quality of the monitoring and the overall safety of the device. While a generic, low cost, wearable battery powered device for such applications may not yield sufficient performance, such devices combined with the capabilities of Artificial Neural Network algorithms can however, prove to be as competent as high end flexible and wearable monitoring devices fabricated using advanced manufacturing technologies. This paper evaluates the feasibility of the Levenberg-Marquardt ANN algorithm for use in any generic low power wearable devices implemented either as a pure real-time embedded system or as an IoT device capable of uploading the monitored readings to the cloud.

翻译：本项目的主要目的是开发一种便携式心脏异常监测设备，该设备能够显著提升监测质量与整体安全性。虽然针对此类应用，采用电池供电的通用低功耗可穿戴设备可能无法达到足够性能，但若此类设备与人工神经网络算法相结合，则可被证实具备与采用先进制造技术生产的高端柔性可穿戴监测设备相媲美的能力。本文评估了Levenberg-Marquardt人工神经网络算法在通用低功耗可穿戴设备中的应用可行性，此类设备既可实现为纯实时嵌入式系统，也可实现为能够将监测读数上传至云端的物联网设备。