Cargando…

Automatic Detection of Dyspnea in Real Human–Robot Interaction Scenarios

A respiratory distress estimation technique for telephony previously proposed by the authors is adapted and evaluated in real static and dynamic HRI scenarios. The system is evaluated with a telephone dataset re-recorded using the robotic platform designed and implemented for this study. In addition...

Descripción completa

Detalles Bibliográficos
Autores principales: Alvarado, Eduardo, Grágeda, Nicolás, Luzanto, Alejandro, Mahu, Rodrigo, Wuth, Jorge, Mendoza, Laura, Stern, Richard M., Yoma, Néstor Becerra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490721/
https://www.ncbi.nlm.nih.gov/pubmed/37688044
http://dx.doi.org/10.3390/s23177590
_version_ 1785103906141896704
author Alvarado, Eduardo
Grágeda, Nicolás
Luzanto, Alejandro
Mahu, Rodrigo
Wuth, Jorge
Mendoza, Laura
Stern, Richard M.
Yoma, Néstor Becerra
author_facet Alvarado, Eduardo
Grágeda, Nicolás
Luzanto, Alejandro
Mahu, Rodrigo
Wuth, Jorge
Mendoza, Laura
Stern, Richard M.
Yoma, Néstor Becerra
author_sort Alvarado, Eduardo
collection PubMed
description A respiratory distress estimation technique for telephony previously proposed by the authors is adapted and evaluated in real static and dynamic HRI scenarios. The system is evaluated with a telephone dataset re-recorded using the robotic platform designed and implemented for this study. In addition, the original telephone training data are modified using an environmental model that incorporates natural robot-generated and external noise sources and reverberant effects using room impulse responses (RIRs). The results indicate that the average accuracy and AUC are just 0.4% less than those obtained with matched training/testing conditions with simulated data. Quite surprisingly, there is not much difference in accuracy and AUC between static and dynamic HRI conditions. Moreover, the beamforming methods delay-and-sum and MVDR lead to average improvement in accuracy and AUC equal to 8% and 2%, respectively, when applied to training and testing data. Regarding the complementarity of time-dependent and time-independent features, the combination of both types of classifiers provides the best joint accuracy and AUC score.
format Online
Article
Text
id pubmed-10490721
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-104907212023-09-09 Automatic Detection of Dyspnea in Real Human–Robot Interaction Scenarios Alvarado, Eduardo Grágeda, Nicolás Luzanto, Alejandro Mahu, Rodrigo Wuth, Jorge Mendoza, Laura Stern, Richard M. Yoma, Néstor Becerra Sensors (Basel) Article A respiratory distress estimation technique for telephony previously proposed by the authors is adapted and evaluated in real static and dynamic HRI scenarios. The system is evaluated with a telephone dataset re-recorded using the robotic platform designed and implemented for this study. In addition, the original telephone training data are modified using an environmental model that incorporates natural robot-generated and external noise sources and reverberant effects using room impulse responses (RIRs). The results indicate that the average accuracy and AUC are just 0.4% less than those obtained with matched training/testing conditions with simulated data. Quite surprisingly, there is not much difference in accuracy and AUC between static and dynamic HRI conditions. Moreover, the beamforming methods delay-and-sum and MVDR lead to average improvement in accuracy and AUC equal to 8% and 2%, respectively, when applied to training and testing data. Regarding the complementarity of time-dependent and time-independent features, the combination of both types of classifiers provides the best joint accuracy and AUC score. MDPI 2023-09-01 /pmc/articles/PMC10490721/ /pubmed/37688044 http://dx.doi.org/10.3390/s23177590 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alvarado, Eduardo
Grágeda, Nicolás
Luzanto, Alejandro
Mahu, Rodrigo
Wuth, Jorge
Mendoza, Laura
Stern, Richard M.
Yoma, Néstor Becerra
Automatic Detection of Dyspnea in Real Human–Robot Interaction Scenarios
title Automatic Detection of Dyspnea in Real Human–Robot Interaction Scenarios
title_full Automatic Detection of Dyspnea in Real Human–Robot Interaction Scenarios
title_fullStr Automatic Detection of Dyspnea in Real Human–Robot Interaction Scenarios
title_full_unstemmed Automatic Detection of Dyspnea in Real Human–Robot Interaction Scenarios
title_short Automatic Detection of Dyspnea in Real Human–Robot Interaction Scenarios
title_sort automatic detection of dyspnea in real human–robot interaction scenarios
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490721/
https://www.ncbi.nlm.nih.gov/pubmed/37688044
http://dx.doi.org/10.3390/s23177590
work_keys_str_mv AT alvaradoeduardo automaticdetectionofdyspneainrealhumanrobotinteractionscenarios
AT gragedanicolas automaticdetectionofdyspneainrealhumanrobotinteractionscenarios
AT luzantoalejandro automaticdetectionofdyspneainrealhumanrobotinteractionscenarios
AT mahurodrigo automaticdetectionofdyspneainrealhumanrobotinteractionscenarios
AT wuthjorge automaticdetectionofdyspneainrealhumanrobotinteractionscenarios
AT mendozalaura automaticdetectionofdyspneainrealhumanrobotinteractionscenarios
AT sternrichardm automaticdetectionofdyspneainrealhumanrobotinteractionscenarios
AT yomanestorbecerra automaticdetectionofdyspneainrealhumanrobotinteractionscenarios