# An Ensemble Mobile-Cloud Computing Method for Affordable and Accurate Glucometer Readout

arXiv: https://arxiv.org/abs/2301.01758 · HTML: https://mlsanity.com/paper/2301.01758/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 4 Jan 2023.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Speed/accuracy trade-offs for modern convolutional object detectors](https://mlsanity.com/paper/1611.10012/) (2016)
- [Distributed Deep Neural Networks over the Cloud, the Edge and End Devices](https://mlsanity.com/paper/1709.01921/) (2017)
- [SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud](https://mlsanity.com/paper/2008.06402/) (2020)
- [ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation](https://mlsanity.com/paper/1812.08934/) (2018)
- [MobileDets: Searching for Object Detection Architectures for Mobile Accelerators](https://mlsanity.com/paper/2004.14525/) (2020)
- [Focus: Querying Large Video Datasets with Low Latency and Low Cost](https://mlsanity.com/paper/1801.03493/) (2018)
- [SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models](https://mlsanity.com/paper/1906.03951/) (2019)

## Abstract (arXiv)

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.