Exploiting Feature Selection in Human Activity Recognition: Methodological Insights and Empirical Results Using Mobile Sensor Data
Human Activity Recognition (HAR) using mobile sensor data has gained increasing attention over the last few years, with a fast-growing number of reported applications. The central role of machine learning in this field has been discussed by a vast amount of research works, with several strategies pr...
Main Authors: | Marco Manolo Manca, Barbara Pes, Daniele Riboni |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2022-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9796510/ |
Similar Items
-
Face Recognition from Small Datasets using Kernel Selection of Gabor Features
by: Alyaa Gadelrab, et al.
Published: (2020-11-01) -
An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition
by: Zhimin Chen, et al.
Published: (2020-04-01) -
MGRA: Motion Gesture Recognition via Accelerometer
by: Feng Hong, et al.
Published: (2016-04-01) -
Human Action Recognition: A Paradigm of Best Deep Learning Features Selection and Serial Based Extended Fusion
by: Seemab Khan, et al.
Published: (2021-11-01) -
Feature Selection on 2D and 3D Geometric Features to Improve Facial Expression Recognition
by: Vianney Perez-Gomez, et al.
Published: (2020-08-01)