Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/s19071556 | SENSORS |
Keywords | Field | DocType |
CNN,deep-learning,classification,human action recognition | Gyroscope,Activity recognition,Pattern recognition,Convolutional neural network,Accelerometer,Electronic engineering,Feature extraction,Movement recognition,Artificial intelligence,Acceleration,Engineering,Deep learning | Journal |
Volume | Issue | ISSN |
19 | 7.0 | 1424-8220 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlos Avilés-Cruz | 1 | 41 | 7.56 |
| Andrés Ferreyra-Ramírez | 2 | 0 | 2.70 |
| Arturo Zúñiga-López | 3 | 0 | 4.06 |
| Juan Villegas-Cortez | 4 | 12 | 5.75 |