Wearables and Machine Learning for Improving Runners' Motivation from an Affective Perspective.

Sandra Baldassarri, Jorge García de Quirós, José Ramón Beltrán, Pedro Álvarez

Abstract 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

–field-weighted citation impact

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.

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

4 authors.

Sandra BaldassarriComputer Science and Systems Engineering Department, Engineering Research Institute of Aragon (I3A), University of Zaragoza, 50018 Zaragoza, Spain.ORCID 0000-0002-9315-6391

Jorge García de QuirósComputer Science and Systems Engineering Department, Engineering Research Institute of Aragon (I3A), University of Zaragoza, 50018 Zaragoza, Spain.

José Ramón BeltránElectronic Engineering and Communications Department, Engineering Research Institute of Aragon (I3A), University of Zaragoza, 50009 Zaragoza, Spain.ORCID 0000-0002-7500-4650

Pedro ÁlvarezComputer Science and Systems Engineering Department, Engineering Research Institute of Aragon (I3A), University of Zaragoza, 50018 Zaragoza, Spain.ORCID 0000-0002-6584-7259

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wearable technology is playing an increasing role in the development of user-centric applications. In the field of sports, this technology is being used to implement solutions that improve athletes' performance, reduce the risk of injury, or control fatigue, for example. Emotions are involved in most of these solutions, but unfortunately, they are not monitored in real-time or used as a decision element that helps to increase the quality of training sessions, nor are they used to guarantee the health of athletes. In this paper, we present a wearable and a set of machine learning models that are able to deduce runners' emotions during their training. The solution is based on the analysis of runners' electrodermal activity, a physiological parameter widely used in the field of emotion recognition. As part of the

Indexed as

Athletic PerformanceMobile ApplicationsWearable Electronic DevicesHumansMachine LearningMotivationemotion recognitionmachine learningmusic recommendationrunningwearable devices

Identifiers

PMID36772647

DOI10.3390/s23031608

PMCPMC9920630

What Socratic 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 Socratic graph.