# Multi-modal Point-of-Care Diagnostics for COVID-19 Based On Acoustics and Symptoms

arXiv: https://arxiv.org/abs/2106.00639 · HTML: https://mlsanity.com/paper/2106.00639/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2021.

## Built on by (most cited first, 4 of 4)

- [Multimodal Detection of COVID-19 Symptoms using Deep Learning & Probability-based Weighting of Modes](https://mlsanity.com/paper/2109.01669/) (2021)
- [Telehealthcare and Telepathology in Pandemic: A Noninvasive, Low-Cost Micro-Invasive and Multimodal Real-Time Online Application for Early Diagnosis of COVID-19 Infection](https://mlsanity.com/paper/2109.07846/) (2021)
- [Coswara: A website application enabling COVID-19 screening by analysing respiratory sound samples and health symptoms](https://mlsanity.com/paper/2206.05053/) (2022)
- [Sustaining model performance for covid-19 detection from dynamic audio data: Development and evaluation of a comprehensive drift-adaptive framework](https://mlsanity.com/paper/2409.19300/) (2024)

## Abstract (arXiv)

The research direction of identifying acoustic bio-markers of respiratory diseases has received renewed interest following the onset of COVID-19 pandemic. In this paper, we design an approach to COVID-19 diagnostic using crowd-sourced multi-modal data. The data resource, consisting of acoustic signals like cough, breathing, and speech signals, along with the data of symptoms, are recorded using a web-application over a period of ten months. We investigate the use of statistical descriptors of simple time-frequency features for acoustic signals and binary features for the presence of symptoms. Unlike previous works, we primarily focus on the application of simple linear classifiers like logistic regression and support vector machines for acoustic data while decision tree models are employed on the symptoms data. We show that a multi-modal integration of acoustics and symptoms classifiers achieves an area-under-curve (AUC) of 92.40, a significant improvement over any individual modality. Several ablation experiments are also provided which highlight the acoustic and symptom dimensions that are important for the task of COVID-19 diagnostics.