Paper Info
Title
Activity recognition on an accelerometer embedded mobile phone with varying positions and orientations
Abstract
This paper uses accelerometer-embedded mobile phones to monitor one's daily physical activities for sake of changing people's sedentary lifestyle. In contrast to the previous work of recognizing user's physical activities by using a single accelerometer-embedded device and placing it in a known position or fixed orientation, this paper intends to recognize the physical activities in the natural setting where the mobile phone's position and orientation are varying, depending on the position, material and size of the hosting pocket. By specifying 6 pocket positions, this paper develops a SVM based classifier to recognize 7 common physical activities. Based on 10-folder cross validation result on a 48.2 hour data set collected from 7 subjects, our solution outperforms Yang's solution and SHPF solution by 5-6%. By introducing an orientation insensitive sensor reading dimension, we boost the overall F-score from 91.5% to 93.1%. With known pocket position, the overall F-score increases to 94.8%.
Year
DOI
Venue
2010
10.1007/978-3-642-16355-5_42
UIC
Keywords
Field
DocType
accelerometer embedded mobile phone,shpf solution,pocket position,known position,known pocket position,mobile phone,orientation insensitive sensor reading,varying position,activity recognition,physical activity,fixed orientation,daily physical activity,common physical activity,svm,cross validation,accelerometer
Computer vision,Activity recognition,Sedentary lifestyle,Accelerometer,Simulation,Computer science,Support vector machine,Computer network,Artificial intelligence,Mobile phone,Classifier (linguistics),Cross-validation
Conference
Volume
ISSN
ISBN
6406
0302-9743
3-642-16354-8
Citations 
PageRank 
References 
89
4.29
16
Authors
5
Name
Order
Citations
PageRank
Lin Sun123410.32
Daqing Zhang23619217.31
Bin Li369450.02
Bin Guo41603143.97
Shijian Li5115569.34