2026-05-02T09:12:51Z https://meral.edu.mm/oai

oai:meral.edu.mm:recid/6162 2022-03-24T23:15:16Z 1582963342780:1596102427017 user-uit

Designing Classifier for Human Activity Recognition Using Artificial Neural Network Win Win Myo Wiphada Wettayaprasit Pattara Aiyarak 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. 2019-02-25 http://hdl.handle.net/20.500.12678/0000006162 https://meral.edu.mm/records/6162