Impact of integrated signals for doing HAR using deep learning models

  1. Jaén Vargas, Milagros
  2. García Martínez, Javier
  3. Reyes Leiva, Karla
  4. Trujillo Guerrero, María Fernanda
  5. Fernandes, Francisco
Libro:
CASEIB 2023. Libro de Actas del XLI Congreso Anual de la Sociedad Española de Ingeniería Biomédica: Contribuyendo a la salud basada en valor
  1. Joaquín Roca González (coord.)
  2. Dolores Ojados González (coord.)
  3. Juan Suardíaz Muro (coord.)

Editorial: Universidad Politécnica de Cartagena

ISBN: 978-84-17853-76-1

Año de publicación: 2023

Páginas: 650-653

Congreso: Congreso Anual de la Sociedad Española de Ingeniería Biomédica. CASEIB (41. 2023. Cartagena)

Tipo: Aportación congreso

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Resumen

Human Activity Recognition (HAR) is having a growing impact in creating new applications and is responsible for emerging new technologies. Also, the use of wearable sensors is an important key to exploring the human body's behavior when performing activities. Hence, the use of these dispositive is less invasive and the person is more comfortable. In this study, a database that includes three activities is used. The activities were acquired from inertial measurement unit sensors (IMU) and motion capture systems (MOCAP). The main objective is differentiating the performance from four Deep Learning (DL) models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and hybrid model Convolutional Neural Network-Long Short-Term Memory (CNNLSTM), when considering acceleration, velocity and position and evaluate if integrating the IMU acceleration to obtain velocity and position represent an increment in performance when it works as input to the DL ...

Fuente de los datos: Dialnet