A Data Processing Method for Human Motion Estimation to Reduce Network and Sensor Node Loads

A Data Processing Method for Human Motion Estimation to Reduce Network and Sensor Node Loads

Shintaro Imai (Graduate School of Software and Information Science, Iwate Prefectural University, Takizawa, Japan), Mariko Miyamoto (Graduate School of Software and Information Science, Iwate Prefectural University, Takizawa, Japan), Mingrui Cai (Graduate School of Software and Information Science, Iwate Prefectural University, Takizawa, Japan), Yoshikazu Arai (Graduate School of Software and Information Science, Iwate Prefectural University, Takizawa, Japan) and Toshimitsu Inomata (Graduate School of Software and Information Science, Iwate Prefectural University, Takizawa, Japan)
Copyright: © 2013 |Pages: 17
DOI: 10.4018/jcini.2013010103

Abstract

Systems for estimating human motion using acceleration sensors present the following two problems: 1) advanced analysis and processing of sensor data are difficult because of resource limitations of sensor nodes; and 2) such analyses and processes burden the network because numerous sensor data are sent to the network. The authors’ proposed method described herein for sensor data analysis and processing uses a host computer located near sensor nodes (neighborhood host). This method is intended to achieve a good balance between reduction of the network load and advanced sensor data analysis and processing. Moreover, this method incorporates reduction of the load to sensor nodes. To evaluate their method, the authors implement two prototype systems that use different machine learning methods. The authors conduct some experiments using these prototype systems. The experimentally obtained results demonstrate that the proposed method can resolve two problems.
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Sensor Data Analysis And Processing At The Neighborhood Host Intended Environment

This study was undertaken to produce a system that provides services to users using human motion estimation. The system acquires acceleration data from sensor nodes attached to an observed person. The nodes acquire acceleration data frequently. The intended environment for this study is presented in Figure 1.

Figure 1.

Intended environment used for this study

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