Paper Info
Title
Analysis And Evaluation Of Smartphone-Based Human Activity Recognition Using A Neural Network Approach
Abstract
It has been more important to measure daily physical activity for several purposes. There have been a number of methods of measuring physical activity, such as self-reporting, attaching wearable sensors, etc. Since a smartphone has become widespread rapidly, physical activity can be easily measured by accelerometers in the smartphone. Although there were a number of studies for activity recognition exploiting smartphone acceleration data, there was little discussion with the influence of each axis of accelerometers for activity recognition. In this paper, we investigate how each axis of smartphone acceleration data is affected on the performance of human activity recognition using a neural network based classifier. Assuming that the smartphone is kept in a pants pocket, the acceleration data of a subject are collected during standing, walking, and running for ten minutes. A multiIayer perceptron was used as an activity classifier to recognize the three activities. Using averages as features, the classifier with the x-axis features provides the best accuracies. Using standard deviations as features, however, the accuracies are better than those using averages.
Year
DOI
Venue
2015
10.1109/IJCNN.2015.7280494
2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
Field
DocType
ISSN
Activity recognition,Pattern recognition,Computer science,Wearable computer,Accelerometer,Multilayer perceptron,Artificial intelligence,Acceleration,Artificial neural network,Classifier (linguistics),Standard deviation,Machine learning
Conference
2161-4393
Citations 
PageRank 
References 
2
0.35
9
Authors
3
Name
Order
Citations
PageRank
Yong-Jin Kwon125028.09
Kyuchang Kang212714.39
Changseok Bae316123.90