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Designing Classifier for Human Activity Recognition Using Artificial Neural Network
http://hdl.handle.net/20.500.12678/0000006162
http://hdl.handle.net/20.500.12678/0000006162758da565-df8d-407e-9244-261454cb6659
5f89a9b7-e45e-4c39-9b6f-2535c1dc5b71
Publication type | ||||||
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Conference paper | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Designing Classifier for Human Activity Recognition Using Artificial Neural Network | |||||
Language | en | |||||
Publication date | 2019-02-25 | |||||
Authors | ||||||
Win Win Myo | ||||||
Wiphada Wettayaprasit | ||||||
Pattara Aiyarak | ||||||
Description | ||||||
Human Activity Recognition (HAR) using built-in sensors in a mobile phone is a very active research area to infer the human daily activities in recent years. The machine learning algorithm is an important role to determine human activities. Although Artificial Neural Network (ANN) is effective classifier to determine the human activity recognition, its performance mainly depends on selection of hidden neuron nodes. In this study, we design a classifier using Multi-Layer Perceptron (MLP) for human activity recognition using UCIHAR dataset. The study introduces a new method to find the hidden neuron nodes for ANN classifier to achieve a better performance of HAR. In this scenario, we also consider the optimal hidden layers and learning rate of ANN after trained the HAR dataset. The simulation result of proposed method 98.32% accuracy is that the proposed model is a better classifier with minimal errors and fixation of hidden neurons for human activity recognition process. |
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Keywords | ||||||
artificial neural network, classifier, human activity recognition, mobile phone; sensors | ||||||
Identifier | 10.1109/CCOMS.2019.8821638 | |||||
Conference papers | ||||||
ICCCS | ||||||
23-25 February, 2019 | ||||||
2019 IEEE 4th International Conference on Computer and Communication Systems | ||||||
Singapore, Singapore | ||||||
https://ieeexplore.ieee.org/xpl/conhome/8811523/proceeding |