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Coarse-Fine Convolutional Deep-Learning Strategy for Human Activity Recognition
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DOI：10.3390/s19071556.png)
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摘要
En 中文
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.
Keyword:
CNN
deep-learning
classification
human action recognition
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期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
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引用论文
Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening使用测试数据锐化的基于分而治之的一维CNN人体活动识别.png)
SENSORSOAAI
SENSORSIF3.5
OAAI分享收藏Feature extraction from smartphone inertial signals for human activity segmentationSIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏Exploratory Data Analysis of Acceleration Signals to Select Light-Weight and Accurate Features for Real-Time Activity Recognition on SmartphonesSENSORSOAAI
SENSORSIF3.5
OAAI分享收藏分享收藏
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