Evidence map›Paper›PMID 37688044›Full record

ArticleSensors (Basel, Switzerland)2023

Automatic Detection of Dyspnea in Real Human-Robot Interaction Scenarios.

Eduardo Alvarado, Nicolás Grágeda, Alejandro Luzanto, Rodrigo Mahu, Jorge Wuth, Laura Mendoza, Richard M Stern, Néstor Becerra Yoma

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 56% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 2 countries.

Eduardo AlvaradoSpeech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile.
Nicolás GrágedaSpeech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile.
Alejandro LuzantoSpeech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile.
Rodrigo MahuSpeech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile.ORCID 0000-0002-6374-8003
Jorge WuthSpeech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile.
Laura MendozaHospital Clínico Universidad de Chile, Santiago 8380420, Chile.
Richard M SternDepartment of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.ORCID 0000-0003-0557-7282
Néstor Becerra YomaSpeech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile.ORCID 0000-0002-3052-4687
University of Chile · CLCarnegie Mellon University · USClínica Alemana · CL

Funding

Agencia Nacional de Investigación y Desarrollo Fondecyt N°1211946.
6 · The paper itself

Abstract

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.

Indexed as

RoboticsDyspneaHumansRecordsbeamforming methodsdeep learninghuman–robot interactionrespiratory-distress evaluation

Identifiers

PMID37688044
PMCPMC10490721
OpenAlexW4386372684

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.