Next Article in Journal
Previous Article in Journal
Sensors 2013, 13(10), 13099-13122; doi:10.3390/s131013099
Article

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

1
, 2
 and 1,*
Received: 26 June 2013; in revised form: 10 September 2013 / Accepted: 18 September 2013 / Published: 27 September 2013
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1180 KB, updated 21 June 2014; original version uploaded 21 June 2014]   |   Browse Figures
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.
Keywords: accelerometer sensor; smartphone; context-awareness; activity recognition; expolatory data analysis; feature extraction accelerometer sensor; smartphone; context-awareness; activity recognition; expolatory data analysis; feature extraction
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Khan, A.M.; Siddiqi, M.H.; Lee, S.-W. Exploratory Data Analysis of Acceleration Signals to Select Light-Weight and Accurate Features for Real-Time Activity Recognition on Smartphones. Sensors 2013, 13, 13099-13122.

AMA Style

Khan AM, Siddiqi MH, Lee S-W. Exploratory Data Analysis of Acceleration Signals to Select Light-Weight and Accurate Features for Real-Time Activity Recognition on Smartphones. Sensors. 2013; 13(10):13099-13122.

Chicago/Turabian Style

Khan, Adil M.; Siddiqi, Muhammad H.; Lee, Seok-Won. 2013. "Exploratory Data Analysis of Acceleration Signals to Select Light-Weight and Accurate Features for Real-Time Activity Recognition on Smartphones." Sensors 13, no. 10: 13099-13122.



Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert