Metodología de detección de anomalías en personas con esclerosis múltiple

  1. Otamendi, Janire 1
  2. Zubizarreta, Asier 1
  3. Mancisidor, Aitziber 1
  1. 1 Universidad del País Vasco/Euskal Herriko Unibertsitatea
    info

    Universidad del País Vasco/Euskal Herriko Unibertsitatea

    Lejona, España

    ROR https://ror.org/000xsnr85

Livre:
XLIV Jornadas de Automática: libro de actas: Universidad de Zaragoza, Escuela de Ingeniería y Arquitectura, 6, 7 y 8 de septiembre de 2023, Zaragoza
  1. Ramón Costa Castelló (coord.)
  2. Manuel Gil Ortega (coord.)
  3. Óscar Reinoso García (coord.)
  4. Luis Enrique Montano Gella (coord.)
  5. Carlos Vilas Fernández (coord.)
  6. Elisabet Estévez Estévez (coord.)
  7. Eduardo Rocón de Lima (coord.)
  8. David Muñoz de la Peña Sequedo (coord.)
  9. José Manuel Andújar Márquez (coord.)
  10. Luis Payá Castelló (coord.)
  11. Alejandro Mosteo Chagoyen (coord.)
  12. Raúl Marín Prades (coord.)
  13. Vanesa Loureiro-Vázquez (coord.)
  14. Pedro Jesús Cabrera Santana (coord.)

Éditorial: Servizo de Publicacións ; Universidade da Coruña

ISBN: 9788497498609

Année de publication: 2023

Pages: 77-82

Congreso: Jornadas de Automática (44. 2023. Zaragoza)

Type: Communication dans un congrès

Résumé

Personalized therapies have proven to be effective in slowing the progression of multiple sclerosis, thereby improving the quality of life of those people suffering from it. However, the design of such therapies requires knowledge of the patient’s functional state and early detection of changes that may occur. Given the drawbacks of traditional assessment techniques, recent studies have proposed monitoring patients’ gait in order to extract relevant indicators and assist specialists in this task. Given this situation, this study proposes a machine learning-based methodology, which aims to detect changes in the functional state of people with multiple sclerosis based on the data provided by a sensorized tip. Taking into account the variability that exists among patients, the proposed design focuses on an individualized approach, which characterizes the state of each individual using only his/her own data. The proposed methodology has been validated in three people with multiple sclerosis, obtaining an average accuracy of 88.9 %.