An Ensemble of Condition Based Classifiers for Device Independent Detailed Human Activity Recognition Using Smartphones

Saha, Jayita, Chowdhury, Chandreyee, Roy Chowdhury, Ishan, Biswas, Suparna and Aslam, Nauman (2018) An Ensemble of Condition Based Classifiers for Device Independent Detailed Human Activity Recognition Using Smartphones. Information, 9 (4). p. 94. ISSN 2078-2489

[img]
Preview
Text
information-09-00094.pdf - Published Version
Available under License Creative Commons Attribution 4.0.

Download (2MB) | Preview
Official URL: http://dx.doi.org/10.3390/info9040094

Abstract

Human activity recognition is increasingly used for medical, surveillance and entertainment applications. For better monitoring, these applications require identification of detailed activity like sitting on chair/floor, brisk/slow walking, running, etc. This paper proposes a ubiquitous solution to detailed activity recognition through the use of smartphone sensors. Use of smartphones for activity recognition poses challenges such as device independence and various usage behavior in terms of where the smartphone is kept. Only a few works address one or more of these challenges. Consequently, in this paper, we present a detailed activity recognition framework for identifying both static and dynamic activities addressing the above-mentioned challenges. The framework supports cases where (i) dataset contains data from accelerometer; and the (ii) dataset contains data from both accelerometer and gyroscope sensor of smartphones. The framework forms an ensemble of the condition based classifiers to address the variance due to different hardware configuration and usage behavior in terms of where the smartphone is kept (right pants pocket, shirt pockets or right hand). The framework is implemented and tested on real data set collected from 10 users with five different device configurations. It is observed that, with our proposed approach, 94% recognition accuracy can be achieved.

Item Type: Article
Uncontrolled Keywords: Human activity recognition; detailed activity; ensemble; device independence; smartphones
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 21 Nov 2018 13:30
Last Modified: 11 Oct 2019 18:06
URI: http://nrl.northumbria.ac.uk/id/eprint/36846

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics