Exploratory Data Analysis of Acceleration Signals to Select Light-Weightand Accurate Features for Real-Time Activity Recognition on Smartphones

Collection with item attached
2013
Item details URL
http://open-repository.kisti.re.kr/cube/handle/open_repository/478748.do
DOI
10.3390/s131013099
Title
Exploratory Data Analysis of Acceleration Signals to Select Light-Weightand Accurate Features for Real-Time Activity Recognition on Smartphones
Description
This work was supported by the National Research Foundation ofKorea(NRF) grant funded by the Korea government(MSIP) (No.2010-0028631).
abstract
Smartphone-based activity recognition (SP-AR) recognizes users' activities using the embedded accelerometer sensor. Only a small number of previous works can be classified as online systems, i.e., the whole process (pre-processing, feature extraction, and classification) is performed on the device. Most of these online systems use either a high sampling rate (SR) or long data-window (DW) to achieve high accuracy, resulting in short battery life or delayed system response, respectively. This paper introduces a real-time/online SP-AR system that solves this problem. Exploratory data analysis was performed on acceleration signals of 6 activities, collected from 30 subjects, to show that these signals are generated by an autoregressive (AR) process, and an accurate AR-model in this case can be built using a low SR (20 Hz) and a small DW (3 s). The high within class variance resulting from placing the phone at different positions was reduced using kernel discriminant analysis to achieve position-independent recognition. Neural networks were used as classifiers. Unlike previous works, true subject-independent evaluation was performed, where 10 new subjects evaluated the system at their homes for 1 week. The results show that our features outperformed three commonly used features by 40% in terms of accuracy for the given SR and DW.
provenance
Made available in Cube on 2018-09-28T12:41:24Z (GMT). No. of bitstreams: 0
language
English
author
Khan, Adil Mehmood
Siddiqi, Muhammad Hameed
Lee, Seok-Won
accessioned
2018-09-28T12:41:24Z
available
2018-09-28T12:41:24Z
issued
2013
citation
SENSORS(13): 10
issn
1424-8220
uri
http://open-repository.kisti.re.kr/cube/handle/open_repository/478748.do
Funder
미래창조과학부
Funding Program
선도연구센터지원
Project ID
1345198165
Jurisdiction
Rep.of Korea
Project Name
Systems Biomedical Informatics Research Center
rights
openAccess
subject
accelerometer sensor
smartphone
context-awareness
activityrecognition
expolatory data analysis
feature extraction
type
article


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