Designing Classifier for Human Activity Recognition Using Artificial Neural Network

Win Win Myo; Wiphada Wettayaprasit; Pattara Aiyarak

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Item

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Designing Classifier for Human Activity Recognition Using Artificial Neural Network

http://hdl.handle.net/20.500.12678/0000006162

Publication type
		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.
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