一种经济实惠且准确的血糖仪读数的嵌入式移动-云计算方法
An Ensemble Mobile-Cloud Computing Method for Affordable and Accurate Glucometer Readout
摘要：
尽管在开发用于定期监测血液特性的先进无线医疗设备方面做出了必要的努力，但在许多国家，许多这样的设备对于每个人来说都是不可用的或负担不起的。或者使用普通设备，患者应该手动将数据记录到移动健康监测中。它会导致几个问题：（1）据报道，客户倾向于输入不切实际的数据;（2）每天多次输入值令人烦恼，并导致客户离开移动应用程序。因此，迫切需要使用现在无处不在的智能手机，通过从医疗设备屏幕捕获图像并自动提取有用信息来减少错误。然而，在其发展过程中存在一些挑战：（1）数据稀缺导致了不切实际的方法，其准确性非常低：据我们所知，在这种情况下只有小数据集可用;（2）准确性-可用性权衡：可以在移动电话上执行不太精确的算法以保持较高的可用性，或者可替换地在云上部署更精确和更计算密集的算法，然而，代价是在连接性差/没有连接性的情况下可用性较低。我们提出了一个集成学习算法，移动云计算服务架构，和一个简单的压缩技术，以实现更高的可用性和更快的响应时间，同时提供更高的准确性，通过集成云和移动端的预测。此外，我们提出了一种算法来生成合成训练数据，这有助于利用深度学习模型来提高准确性。我们提出的方法实现了三个主要目标：（1）在两个不同数据集上的准确率分别为92.1%和97.7%，将以前的方法提高了40%，（2）将所需带宽减少了45倍，准确率下降了1%，（3）与仅移动，仅云，拆分计算和早期退出服务模型相比，提供了更好的可用性。
英文摘要：
Despite essential efforts towards advanced wireless medical devices for regular monitoring of blood properties, many such devices are not available or not affordable for everyone in many countries. Alternatively using ordinary devices, patients ought to log data into a mobile health-monitoring manually. It causes several issues: (1) clients reportedly tend to enter unrealistic data; (2) typing values several times a day is bothersome and causes clients to leave the mobile app. Thus, there is a strong need to use now-ubiquitous smartphones, reducing error by capturing images from the screen of medical devices and extracting useful information automatically. Nevertheless, there are a few challenges in its development: (1) data scarcity has led to impractical methods with very low accuracy: to our knowledge, only small datasets are available in this case; (2) accuracy-availability tradeoff: one can execute a less accurate algorithm on a mobile phone to maintain higher availability, or alternatively deploy a more accurate and more compute-intensive algorithm on the cloud, however, at the cost of lower availability in poor/no connectivity situations. We present an ensemble learning algorithm, a mobile-cloud computing service architecture, and a simple compression technique to achieve higher availability and faster response time while providing higher accuracy by integrating cloud- and mobile-side predictions. Additionally, we propose an algorithm to generate synthetic training data which facilitates utilizing deep learning models to improve accuracy. Our proposed method achieves three main objectives: (1) 92.1% and 97.7% accuracy on two different datasets, improving previous methods by 40%, (2) reducing required bandwidth by 45x with 1% drop in accuracy, (3) and providing better availability compared to mobile-only, cloud-only, split computing, and early exit service models.
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