Wearables and machine learning for improving runners’ motivation from an affective perspective

Baldassarri, Sandra (Universidad de Zaragoza) ; García de Quirós, Jorge (Universidad de Zaragoza) ; Beltrán, José Ramón (Universidad de Zaragoza) ; Álvarez, Pedro (Universidad de Zaragoza)

Resumen: 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 DJ-Running project, we have used these emotions to increase runners’ motivation through music. It has required integrating the wearable and the models into the DJ-Running mobile application, which interacts with the technological infrastructure of the project to select and play the most suitable songs at each instant of the training.

Idioma: Inglés

DOI: 10.3390/s23031608

Año: 2023

Publicado en: Sensors 23, 3 (2023), 1608 [16 pp]
ISSN: 1424-8220

Factor impacto JCR: 3.4 (2023)

Categ. JCR: CHEMISTRY, ANALYTICAL rank: 34 / 106 = 0.321 (2023) - Q2 - T1
Categ. JCR: INSTRUMENTS & INSTRUMENTATION rank: 24 / 76 = 0.316 (2023) - Q2 - T1
Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 122 / 352 = 0.347 (2023) - Q2 - T2

Factor impacto CITESCORE: 7.3 - Atomic and Molecular Physics, and Optics (Q1) - Electrical and Electronic Engineering (Q1) - Analytical Chemistry (Q1) - Information Systems (Q1) - Instrumentation (Q1) - Biochemistry (Q2)

Factor impacto SCIMAGO: 0.786 - Instrumentation (Q1) - Analytical Chemistry (Q1) - Atomic and Molecular Physics, and Optics (Q1) - Information Systems (Q2) - Medicine (miscellaneous) (Q2) - Biochemistry (Q2) - Electrical and Electronic Engineering (Q2)

Financiación: info:eu-repo/grantAgreement/ES/MINECO/PDC2021-121072-C22
Financiación: info:eu-repo/grantAgreement/ES/RTI2018-096986-B-C31
Financiación: info:eu-repo/grantAgreement/EUR/MINECO/TED2021-130374B-C22

Tipo y forma: Article (Published version)

Área (Departamento): Área Tecnología Electrónica (Dpto. Ingeniería Electrón.Com.)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)