NDLI: The human movement identification using the radio signal strength in WBAN

Content Provider	IEEE Xplore Digital Library
Author	Archasantisuk, S. Aoyagi, T.
Copyright Year	2015
Description	Author affiliation: Grad. Sch. of Decision Sci. & Technol., Tokyo Inst. of Technol., Tokyo, Japan (Archasantisuk, S.; Aoyagi, T.)
Abstract	This paper investigated the feasibility of using the radio signal strength of sensors placed around the human body in the human movement identification. This proposed method can identify the human movement in WBAN using only the radio signal strength, thus any additional tools are not necessary. OpenNICTA provides the BAN measurement channel in three kinds of human motions, which are running, walking and standing. This paper used three sets of the measurement data, which Tx-Rx located at Back-Chest, RightAnkle-Chest, and RightWrist-Chest. Each data set was separately used to identify the movements. This paper used two types of machine learning, which are neural network and decision tree. In the neural network, it has been found that using eight types of features, which are SCP, Range, SSI, RMS, LCR, SC, WAMP, Histogram, calculated from 200 continuous received signal levels can identify the human movements with accuracy rate of 90.41-98.83 percent. Using the same features, the decision tree can identify the human movements with the accuracy rate of 99.04-99.66 percent. Both tools perform well on the human movement identification. However, the decision tree outperforms the neural network in this task.
Starting Page	59
Ending Page	63
File Size	307647
Page Count	5
File Format	PDF
ISBN	9781479980727
ISSN	23268301
DOI	10.1109/ISMICT.2015.7107498
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-03-24
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Legged locomotion Accuracy Neural Network Decision Tree Body area networks Sensors Decision trees Biological neural networks Wireless Body Area Network Human Movement Identification
Content Type	Text
Resource Type	Article

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