SUPAR: Smartphone as a ubiquitous physical activity recognizer for u-healthcare services

Current generation smartphone can be seen as one of the most ubiquitous device for physical activity recognition. In this paper we proposed a physical activity recognizer to provide u-healthcare services in a cost effective manner by utilizing cloud computing infrastructure. Our model is comprised o...

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Bibliographic Details
Main Authors: Fahim, Muhammad, Sungyoung Lee, Yongik Yoon
Format: Conference Proceeding
Language:English
Subjects:
Accelerometers
Cloud computing
Feature extraction
Legged locomotion
Medical services
Servers
Support vector machines
Online Access:Request full text
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Summary:Current generation smartphone can be seen as one of the most ubiquitous device for physical activity recognition. In this paper we proposed a physical activity recognizer to provide u-healthcare services in a cost effective manner by utilizing cloud computing infrastructure. Our model is comprised on embedded triaxial accelerometer of the smartphone to sense the body movements and a cloud server to store and process the sensory data for numerous kind of services. We compute the time and frequency domain features over the raw signals and evaluate different machine learning algorithms to identify an accurate activity recognition model for four kinds of physical activities (i.e., walking, running, cycling and hopping). During our experiments we found Support Vector Machine (SVM) algorithm outperforms for the aforementioned physical activities as compared to its counterparts. Furthermore, we also explain how smartphone application and cloud server communicate with each other.
ISSN:1094-687X
1558-4615
2694-0604
DOI:10.1109/EMBC.2014.6944418