Publications by Lara Orlandic | EPFL Graph Search

Cough-E: A multimodal, privacy-preserving cough detection algorithm for the edge

David Atienza Alonso, Tomas Teijeiro Campo, Lara Orlandic, Jonathan Dan, Jérôme Paul Rémy Thevenot, Stefano Albini

Continuous cough monitors can greatly benefit doctors in home monitoring and treatment of respiratory diseases. Although many works propose algorithms to automate this task, they suffer of poor data privacy and short-term monitoring. Edge-AI is a promising ...

2025

An MLIR-based Compilation Framework for CGRA Application Deployment

David Atienza Alonso, Giovanni Ansaloni, Lara Orlandic, Rubén Rodríguez Álvarez, Juan Pablo Sapriza Araujo

Coarse-Grained Reconfigurable Arrays (CGRAs) are a programmable architectural solution that efficiently supports the execution of computation-intensive application functions (kernels). Compilers for CGRAs typically focus on optimizing the mapping of single ...

2025

How to Count Coughs: An Event-Based Framework for Evaluating Automatic Cough Detection Algorithm Performance

David Atienza Alonso, Tomas Teijeiro Campo, Lara Orlandic, Jonathan Dan, Jérôme Paul Rémy Thevenot

Chronic cough disorders are widespread and challenging to assess because they rely on subjective patient questionnaires about cough frequency. Wearable devices running Machine Learning (ML) algorithms are promising for quantifying daily coughs, providing c ...

IEEE2024

ACE: Automated optimization towards iterative Classification in Edge health monitors

David Atienza Alonso, Giovanni Ansaloni, Lara Orlandic, Simone Machetti

Wearable devices for health monitoring are essential for tracking individuals' health status and facilitating early detection of diseases. However, processing biomedical signals online for real-time monitoring is challenging due to limited computational re ...

Institute of Electrical and Electronics Engineers (IEEE)2024

A semi-supervised algorithm for improving the consistency of crowdsourced datasets: The COVID-19 case study on respiratory disorder classification

David Atienza Alonso, Tomas Teijeiro Campo, Lara Orlandic

Background and Objective: Cough audio signal classification is a potentially useful tool in screening for respiratory disorders, such as COVID-19. Since it is dangerous to collect data from patients with contagious diseases, many research teams have turned ...

2023

A Multimodal Dataset for Automatic Edge-AI Cough Detection

David Atienza Alonso, Tomas Teijeiro Campo, Lara Orlandic, Jérôme Paul Rémy Thevenot

Counting the number of times a patient coughs per day is an essential biomarker in determining treatment efficacy for novel antitussive therapies and personalizing patient care. Automatic cough counting tools must provide accurate information, while runnin ...

New York2023

A Multimodal Dataset for Automatic Edge-AI Cough Detection

David Atienza Alonso, Tomas Teijeiro Campo, Lara Orlandic, Jérôme Paul Rémy Thevenot

Counting the number of times a patient coughs per day is an essential biomarker in determining treatment efficacy for novel antitussive therapies and personalizing patient care. There is a need for wearable devices that employ multimodal sensors to perform ...

Zenodo2023

Hybrid Handcrafted and Learnable Audio Representation for Analysis of Speech Under Cognitive and Physical Load

Lara Orlandic

As a neurophysiological response to threat or adverse conditions, stress can affect cognition, emotion and behaviour with potentially detrimental effects on health in the case of sustained exposure. Since the affective content of speech is inherently modul ...

ISCA-INT SPEECH COMMUNICATION ASSOC2022

Wearable and Continuous Prediction of Passage of Time Perception for Monitoring Mental Health

David Atienza Alonso, Lara Orlandic, Adriana Arza Valdes

A person’s passage of time perception (POTP) is strongly linked to their mental state and stress response, and can therefore provide an easily quantifiable means of continuous mental health monitoring. In this work, we develop a custom experiment and Machi ...

2021

The COUGHVID crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms

David Atienza Alonso, Tomas Teijeiro Campo, Lara Orlandic

Cough audio signal classification has been successfully used to diagnose a variety of respiratory conditions, and there has been significant interest in leveraging Machine Learning (ML) to provide widespread COVID-19 screening. The COUGHVID dataset provide ...

2021