Deep Neural Networks for Human Activity Recognition With Wearable Sensors: Leave-One-Subject-Out Cross-Validation for Model Selection

Deep Neural Networks for Human Activity Recognition With Wearable Sensors: Leave-One-Subject-Out Cross-Validation for Model Selection

Human Activity Recognition (HAR) has been attracting significant research attention because of the increasing availability of environmental and wearable sensors for collecting HAR data. In recent years, deep learning approaches have demonstrated a great success due to their ability to model complex...

Full description

Bibliographic Details
Main Authors: Davoud Gholamiangonabadi, Nikita Kiselov, Katarina Grolinger
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Deep neural networks
human activity recognition
model selection
convolutional neural networks
feed forward neural networks
model evaluation
Online Access:https://ieeexplore.ieee.org/document/9144538/

Internet

https://ieeexplore.ieee.org/document/9144538/

Similar Items