  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.
