Open AccessHybrid Model–Based Motion Recognition for Smartphone Users
Author(s) -
Shin Beomju,
Kim Chulki,
Kim Jae Hun,
Lee Seok,
Kee Changdon,
Lee Taikjin
Publication year - 2014
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.14.0113.1159
Subject(s) - classifier (uml) , computer science , artificial neural network , artificial intelligence , motion (physics) , decision tree , activity recognition , field (mathematics) , pattern recognition (psychology) , machine learning , speech recognition , mathematics , pure mathematics
This paper presents a hybrid model solution for user motion recognition. The use of a single classifier in motion recognition models does not guarantee a high recognition rate. To enhance the motion recognition rate, a hybrid model consisting of decision trees and artificial neural networks is proposed. We define six user motions commonly performed in an indoor environment. To demonstrate the performance of the proposed model, we conduct a real field test with ten subjects (five males and five females). Experimental results show that the proposed model provides a more accurate recognition rate compared to that of other single classifiers.