# CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks

arXiv: https://arxiv.org/abs/2007.10497 · HTML: https://mlsanity.com/paper/2007.10497/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 7 of 7)

- [Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects](https://mlsanity.com/paper/2312.17004/) (2023)
- [Boundary Guided Semantic Learning for Real-time COVID-19 Lung Infection Segmentation System](https://mlsanity.com/paper/2209.02934/) (2022)
- [DOCTOR: A Multi-Disease Detection Continual Learning Framework Based on Wearable Medical Sensors](https://mlsanity.com/paper/2305.05738/) (2023)
- [CONFINE: Conformal Prediction for Interpretable Neural Networks](https://mlsanity.com/paper/2406.00539/) (2024)
- [MHDeep: Mental Health Disorder Detection System based on Body-Area and Deep Neural Networks](https://mlsanity.com/paper/2102.10435/) (2021)
- [PAGE: Domain-Incremental Adaptation with Past-Agnostic Generative Replay for Smart Healthcare](https://mlsanity.com/paper/2403.08197/) (2024)
- [COMFORT: A Continual Fine-Tuning Framework for Foundation Models Targeted at Consumer Healthcare](https://mlsanity.com/paper/2409.09549/) (2024)

## Abstract (arXiv)

The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands, and also suffers from a relatively low positive detection rate in the early stages of the resultant COVID-19 disease. Hence, there is a need for an alternative approach for repeated large-scale testing of SARS-CoV-2/COVID-19. We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus. We collected data from 87 individuals, spanning three cohorts including healthy, asymptomatic, and symptomatic patients. We trained DNNs on various subsets of the features automatically extracted from six WMS and questionnaire categories to perform ablation studies to determine which subsets are most efficacious in terms of test accuracy for a three-way classification. The highest test accuracy obtained was 98.1%. We also augmented the real training dataset with a synthetic training dataset drawn from the same probability distribution to impose a prior on DNN weights and leveraged a grow-and-prune synthesis paradigm to learn both DNN architecture and weights. This boosted the accuracy of the various DNNs further and simultaneously reduced their size and floating-point operations.