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Sensors 2013, 13(10), 13099-13122; doi:10.3390/s131013099

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

1
Division of Information and Computer Engineering, Ajou University, San 5 Woncheon-dong, Suwon 443-749, Korea
2
Department of Computer Engineering, Kyung Hee University, Suwon 446-701, Korea
*
Author to whom correspondence should be addressed.
Received: 26 June 2013 / Revised: 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, 21 June 2014; original version 21 June 2014]   |  

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. View Full-Text
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 (CC BY 3.0).

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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.

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