PDF(Pubmed)
Abstract：
This paper outlines the development of the \'Cardiac Abnormality Monitoring\' wearable medical device, aimed at creating a compact safety monitor integrating advanced Artificial Neural Network (ANN) algorithms. Given power consumption constraints and cost-effectiveness, a strategy combining sophisticated instruments with neural network algorithms is proposed to enhance performance. This approach aims to compete with high-end wearable devices, utilizing innovative manufacturing techniques. The paper evaluates the feasibility of employing the Levenberg-Marquardt (LM) ANN algorithm in power-conscious wearable devices, considering its potential for offline embedded systems or IoT gadgets capable of cloud-based data uploading. The Levenberg-Marquardt ANN is chosen primarily for its practicality in prototype development, with other neural network algorithms also explored to identify potential alternatives. We have compared the six neural network models and determined the model that has the potential to replace the primary neural network model. We found that the \'Kernelized SVC with PCA\' can test accuracy. To be specific, in this paper, we will evaluate the performance of the ANN model and also check its feasibility and practicality by integrating it with a constructed prototypical working model.
摘要：
本文概述了“心脏异常监测”可穿戴医疗设备的发展，旨在创建一个紧凑的安全监视器，集成先进的人工神经网络（ANN）算法。考虑到功耗限制和成本效益，提出了一种将复杂仪器与神经网络算法相结合的策略来提高性能。这种方法旨在与高端可穿戴设备竞争，利用创新的制造技术。本文评估了在具有功耗意识的可穿戴设备中采用Levenberg-Marquardt（LM）ANN算法的可行性，考虑到它在离线嵌入式系统或能够基于云的数据上传的物联网小工具方面的潜力。选择Levenberg-MarquardtANN主要是因为其在原型开发中的实用性，与其他神经网络算法也进行了探索，以识别潜在的替代品。我们已经比较了六种神经网络模型，并确定了有可能取代初级神经网络模型的模型。我们发现，“带有PCA的内核化SVC”可以测试准确性。具体而言,在本文中，我们将评估ANN模型的性能，并通过将其与构建的原型工作模型集成来检查其可行性和实用性。
