Format

Send to

Choose Destination
Sensors (Basel). 2019 Mar 31;19(7). pii: E1556. doi: 10.3390/s19071556.

Coarse-Fine Convolutional Deep-Learning Strategy for Human Activity Recognition.

Author information

1
Autonomous Metropolitan University. Electronics Department, Av. San Pablo 180, Col. Reynosa, C.P. 02200 Mexico City, Mexico. caviles@azc.uam.mx.
2
Autonomous Metropolitan University. Electronics Department, Av. San Pablo 180, Col. Reynosa, C.P. 02200 Mexico City, Mexico. fra@azc.uam.mx.
3
Autonomous Metropolitan University. Electronics Department, Av. San Pablo 180, Col. Reynosa, C.P. 02200 Mexico City, Mexico. azl@azc.uam.mx.
4
Autonomous Metropolitan University. Electronics Department, Av. San Pablo 180, Col. Reynosa, C.P. 02200 Mexico City, Mexico. juanvc@azc.uam.mx.

Abstract

In the last decade, deep learning techniques have further improved human activity recognition (HAR) performance on several benchmark datasets. This paper presents a novel framework to classify and analyze human activities. A new convolutional neural network (CNN) strategy is applied to a single user movement recognition using a smartphone. Three parallel CNNs are used for local feature extraction, and latter they are fused in the classification task stage. The whole CNN scheme is based on a feature fusion of a fine-CNN, a medium-CNN, and a coarse-CNN. A tri-axial accelerometer and a tri-axial gyroscope sensor embedded in a smartphone are used to record the acceleration and angle signals. Six human activities successfully classified are walking, walking-upstairs, walking-downstairs, sitting, standing and laying. Performance evaluation is presented for the proposed CNN.

KEYWORDS:

CNN; classification; deep-learning; human action recognition

PMID:
30935117
PMCID:
PMC6480225
DOI:
10.3390/s19071556
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Multidisciplinary Digital Publishing Institute (MDPI) Icon for PubMed Central
Loading ...
Support Center