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Information 2018, 9(4), 94; https://doi.org/10.3390/info9040094

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

1
Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
2
Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata 700064, India
3
Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
This paper is an extended version of our paper published in Proceedings of e-Health Pervasive Wireless Applications and Services (e-HPWAS’17), Rome, Italy, 9 OCtober 2017.
*
Author to whom correspondence should be addressed.
Received: 25 January 2018 / Revised: 11 April 2018 / Accepted: 12 April 2018 / Published: 16 April 2018
(This article belongs to the Special Issue e-Health Pervasive Wireless Applications and Services (e-HPWAS'17))
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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. View Full-Text
Keywords: human activity recognition; detailed activity; ensemble; device independence; smartphones human activity recognition; detailed activity; ensemble; device independence; smartphones
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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 (CC BY 4.0).
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Saha, J.; Chowdhury, C.; Roy Chowdhury, I.; Biswas, S.; Aslam, N. An Ensemble of Condition Based Classifiers for Device Independent Detailed Human Activity Recognition Using Smartphones . Information 2018, 9, 94.

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