Object Detection for Functional Assessment Applications
Authors
Melino Carrero, Alessandro; Nieva Suárez, Álvaro; Losada Gutiérrez, Cristina; Marrón Romera, Marta; Guardiola Luna, Irene; [et al.]Identifiers
Permanent link (URI): http://hdl.handle.net/10017/57846DOI: 10.1007/978-3-031-34204-2_28
ISBN: 978-3-031-34204-2
Publisher
Springer Nature
Date
2023-06-07Embargo end date
2024-07-01Funders
Agencia Estatal de Investigación
Universidad de Alcalá
Bibliographic citation
Melino Carrero, A., Nieva Suárez, A., Losada Gutiérrez, C., Marron Romera, M., Guardiola Luna, I. & Baeza Mas, J. 2023, “Object detection for functional assessment applications”, in Engineering Applications of Neural Networks, EANN 2023. Communications in Computer and Information Science, vol. 1826, pp. 328-339.
Keywords
Image processing
Object detection
Occupational therapy
Functional assessment
Human-Object Interaction
Description / Notes
Engineering Applications of Neural Networks, EANN 2023
Project
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113118RB-C31/ES/ANALISIS MULTISENSORIAL DE LA ACTIVIDAD HUMANA PARA EL DIAGNOSTICO Y LA DETECCION TEMPRANA DE LIMITACIONES FUNCIONALES-UAH/
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-115995RB-I00/ES/TECNICAS DE APRENDIZAJE PARA RESOLVER LA RECONSTRUCCION Y REGISTRO DEFORMABLES APLICADOS A IMAGENES DE LAPAROSCOPIA/
info:eu-repo/grantAgreement/UAH//CM-JIN-2021-015
info:eu-repo/grantAgreement/UAH//PIUAH21%2FIA-016
info:eu-repo/grantAgreement/UAH//PIUAH22%2FIA-037
Document type
info:eu-repo/semantics/conferenceObject
Version
info:eu-repo/semantics/submittedVersion
Publisher's version
https://doi.org/10.1007/978-3-031-34204-2_28Rights
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
© 2023 The authors, under exclusive license to Springer Nature Switzerland AG
Access rights
info:eu-repo/semantics/embargoedAccess
Abstract
This paper presents a proposal for object detection as a first stage for the analysis of Human-Object Interaction (HOI) in the context of automated functional assessment. The proposed system is based in a two-step strategy, thus, in the first stage there are detected the people in the scene, as well as large objects (table, chairs, etc.) using a pre-trained YOLOv8. Then, there is defined a ROI around each person that is processed using a custom YOLO to detect small elements (forks, plates, spoons, etc.). Since there are no large image datasets that include all the objects of interest, there has also been compiled a new dataset including images from different sets, and improving the available labels. The proposal has been evaluated in the novel dataset, and in different images acquired in the area in which the functional assessment is performed, obtaining promising results.
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