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Context-Aware Data Analytics for Activity Recognition

Authors:
Mohammad Pourhomayoun
Ebrahim Nemati
Bobak Mortazavi
Majid Sarrafzadeh

Keywords: Activity Recognition; Indoor Localization.

Abstract:
Remote Health Monitoring Systems are gaining an important role in healthcare by collecting and transmitting patient information and providing data analytics techniques to analyze the collected data and extract knowledge. Physical activity recognition and indoor localization are two of the most important concepts in assistive healthcare, where tracking the positions, motions and reactions of a patient or elderly is required for medical observation or accident prevention. In this paper, we propose a novel context-aware data analytics framework to classify and recognize the physical activity based on the signals received from a worn SmartWatch, the location information of the human subject, and advanced machine learning algorithms. In this approach, we take into account the physical location of the human subject as contextual information to improve the accuracy of the activity classification. The hypothesis is that the location information can get involved in classifier decision making as a prior probability distribution to help improve the accuracy of activity recognition. The results demonstrate improvements in accuracy and performance of the activity classification when applying the proposed method compared to conventional classifications.

Pages: 63 to 68

Copyright: Copyright (c) IARIA, 2015

Publication date: July 19, 2015

Published in: conference

ISSN: 2308-4464

ISBN: 978-1-61208-423-7

Location: Nice, France

Dates: from July 19, 2015 to July 24, 2015