[Paper Review] A Novel AI-enabled Framework to Diagnose Coronavirus COVID 19 using Smartphone Embedded Sensors: Design Study

Halgurd S. Maghdid, Kayhan Zrar Ghafoor+4 more|arXiv (Cornell University)|Mar 16, 2020

COVID-19 diagnosis using AI70 citations

TL;DR

The paper proposes an AI-enabled framework that uses smartphone embedded sensors to detect COVID-19 and predict pneumonia severity as a design study.

ABSTRACT

Coronaviruses are a famous family of viruses that cause illness in both humans and animals. The new type of coronavirus COVID-19 was firstly discovered in Wuhan, China. However, recently, the virus has widely spread in most of the world and causing a pandemic according to the World Health Organization (WHO). Further, nowadays, all the world countries are striving to control the COVID-19. There are many mechanisms to detect coronavirus including clinical analysis of chest CT scan images and blood test results. The confirmed COVID-19 patient manifests as fever, tiredness, and dry cough. Particularly, several techniques can be used to detect the initial results of the virus such as medical detection Kits. However, such devices are incurring huge cost, taking time to install them and use. Therefore, in this paper, a new framework is proposed to detect COVID-19 using built-in smartphone sensors. The proposal provides a low-cost solution, since most of radiologists have already held smartphones for different daily-purposes. Not only that but also ordinary people can use the framework on their smartphones for the virus detection purposes. Nowadays Smartphones are powerful with existing computation-rich processors, memory space, and large number of sensors including cameras, microphone, temperature sensor, inertial sensors, proximity, colour-sensor, humidity-sensor, and wireless chipsets/sensors. The designed Artificial Intelligence (AI) enabled framework reads the smartphone sensors signal measurements to predict the grade of severity of the pneumonia as well as predicting the result of the disease.

Motivation & Objective

• Motivate a low-cost alternative to conventional COVID-19 diagnostics using ubiquitous smartphone hardware.
• Propose an AI-driven framework that reads sensor measurements from smartphones to predict disease presence and pneumonia severity.
• Highlight feasibility, design considerations, and potential benefits of smartphone-based diagnostics for pandemic response.

Proposed method

• Leverages built-in smartphone sensors (camera, microphone, temperature, inertial, proximity, color, humidity, wireless) to capture health-related signals.
• Develops an AI-enabled framework that processes sensor data to predict COVID-19 status and pneumonia severity.
• Argues for a low-cost, accessible solution that can be used by ordinary people on their smartphones.

Experimental results

Research questions

• RQ1 Can smartphone embedded sensors provide discriminative signals for COVID-19 diagnosis?
• RQ2 Can the framework predict the severity of pneumonia associated with COVID-19 from sensor data?
• RQ3 What are the design considerations and feasibility challenges of deploying AI on mobile devices for infectious disease detection?

Key findings

• The work presents a design study framing a low-cost smartphone-based approach to COVID-19 detection.
• The framework reads multi-sensor signals from smartphones to predict disease outcome and pneumonia severity.
• It discusses the potential benefits and limitations of deploying AI-enabled diagnostics on consumer devices.