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Authors: Bingchuan Yuan ; John Herbert and Yalda Emamian

Affiliation: University College Cork, Ireland

ISBN: 978-989-758-000-0

Keyword(s): ADLs, Smartphone, Wearable Wireless Sensor, Machine Learning, Cloud Infrastructure, Unsupervised Learning, Real-time Activity Recognition.

Related Ontology Subjects/Areas/Topics: Context ; Context-Aware Applications ; Detection and Estimation ; Digital Signal Processing ; Mobile and Pervasive Computing ; Mobile Computing ; Paradigm Trends ; Pervasive Health ; Software Engineering ; Telecommunications

Abstract: Learning and recognizing the activities of daily living (ADLs) of an individual is vital when providing an individual with context-aware at-home healthcare. In this work, unobtrusive detection of inhabitants’ activities in the home environment is implemented through the smartphone and wearable wireless sensor belt solution. A hybrid classifier is developed by combining threshold-based methods and machine learning mechanisms. Features extracted from the raw inertial sensor data are collected from a Body Area Network (BAN) (consisting of the Zephyr BioHarness sensor and an Android smartphone), and are used to build classification models using different machine learning algorithms. A cloud-based data analytics framework is developed to process different classification models in parallel and to select the most suitable model for each user. The evolving machine learning mechanism makes the model become customizable and self-adaptive by utilizing a cloud infrastructure which also overcomes the limitation of the computing power and storage of a smartphone. Furthermore, we investigate methods for adapting a universal model, which is trained using the data set of all users, to an individual user through an unsupervised learning scheme. The evaluation results of the experiments conducted on eight participants indicate that the proposed approach can robustly identify activities in real-time across multiple individuals: the highest recognition rate achieved 98% after a few runs. (More)

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Paper citation in several formats:
Yuan, B.; Herbert, J. and Emamian, Y. (2014). Smartphone-based Activity Recognition using Hybrid Classifier - Utilizing Cloud Infrastructure for Data Analysis.In Proceedings of the 4th International Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PECCS, ISBN 978-989-758-000-0, pages 14-23. DOI: 10.5220/0004723900140023

@conference{peccs14,
author={Bingchuan Yuan. and John Herbert. and Yalda Emamian.},
title={Smartphone-based Activity Recognition using Hybrid Classifier - Utilizing Cloud Infrastructure for Data Analysis},
booktitle={Proceedings of the 4th International Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PECCS,},
year={2014},
pages={14-23},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004723900140023},
isbn={978-989-758-000-0},
}

TY - CONF

JO - Proceedings of the 4th International Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PECCS,
TI - Smartphone-based Activity Recognition using Hybrid Classifier - Utilizing Cloud Infrastructure for Data Analysis
SN - 978-989-758-000-0
AU - Yuan, B.
AU - Herbert, J.
AU - Emamian, Y.
PY - 2014
SP - 14
EP - 23
DO - 10.5220/0004723900140023

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