Departamento de electronica Universidad de Alcala

Líneas de investigación

Accede a información sobre la estructura de la actividad investigadora de Geintra.

Trabaja con nosotros

Accede a nuestra oferta actual de becas, tesis doctorales, contratos y trabajos fin de carrera.

Contacta con el grupo

Si desea contactar con nosotros, puede usar varios medios.

    Towards Automatic Assessment of Quiet Standing Balance During the Execution of ADLs

    TítuloTowards Automatic Assessment of Quiet Standing Balance During the Execution of ADLs
    Tipo de publicaciónConference Paper
    Año de publicación2023
    AutoresGuardiola-Luna, I, Monasterio-Exposito, L, Macias-Guarasa, J, Nieva-Suarez, A, Murillo-Teruel, M, Martin-Sanchez, JL, Palazuelos-Cagigas, SE
    Idioma de publicaciónEnglish
    Conference NameEngineering Applications of Neural Networks
    EditorialSpringer Nature Switzerland
    Conference LocationCham
    Fecha de publicación07/2023
    Numero ISBN978-3-031-34204-2

    The current method used to estimate the balance a person has during the performance of Activities of Daily Life (ADLs) is through the application of standardized scales used by occupational therapists to evaluate a person's motor skills and performance quality during those activities, such as the Assessment of Motor and Process Skills scale (AMPS). In this paper, we propose a method to automate the evaluation of a person's balance during the stage of quiet standing still while a person is completing an ADL. Our proposal is aimed to first estimate the projection of the person's center of mass (CoM) from the 3D position of the body joints by applying theoretical and deep learning approaches. Then, we aim to predict a clinically validated objective balance score from previous estimations of the CoM and the Center of Pressure (CoP), using different neural network models. While there are other proposals in the literature, the lack of publicly available datasets makes it difficult to do an extensive comparison, so we compare our proposal with state-of-the-art results in two publicly available datasets, improving their results.

    Geintra © 2008-2024

    Diseño web por Hazhistoria