Presentation 2014-05-30
Human Movement Classification using Signal Level Fluctuation in WBAN at 403.5 MHz and 2.45 GHz
Sukhumarn ARCHASANTISUK, Takahiro AOYAGI, Tero UUSITUPA,
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Abstract(in English) There are many requirements for Body Area Network (BAN) system. BAN system needs to be reliable since it is often used for carrying current body status of patients or elderly people. One challenge of BAN is that the reliability of the system is suffered from intentional movements and unintentional movements. Therefore, the human movement should be considered in order to maintain the quality of BAN system. Various movements cause signal fluctuation differently, so the human movement could be determined by analyzing the received radio signal level. This paper aims to present that the human movement can be recognized by a temporal received signal level. A neural network is applied as a classification tool. The human movement data is based on a numerical simulation generated in 2 frequency bands, which are 403.5 MHz and 2.45 GHz. The result shows that the neural network using the received signal level form six sensors concurrently can classify the human movement well. The accuracy measured by test set data is around 90 percent. However, the accuracy reduces to around 66 percent when the neural network uses the received signal level form one sensor.
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Keyword(in English) MICT / WBAN / Human Movement / Classification / Neural Network
Paper # RCC2014-15,MICT2014-15
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Conference Information
Committee RCC
Conference Date 2014/5/22(1days)
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Registration To Reliable Communication and Control (RCC)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Human Movement Classification using Signal Level Fluctuation in WBAN at 403.5 MHz and 2.45 GHz
Sub Title (in English)
Keyword(1) MICT
Keyword(2) WBAN
Keyword(3) Human Movement
Keyword(4) Classification
Keyword(5) Neural Network
1st Author's Name Sukhumarn ARCHASANTISUK
1st Author's Affiliation Graduate School of Decision Science and Technology, Tokyo Institute of Technology()
2nd Author's Name Takahiro AOYAGI
2nd Author's Affiliation Graduate School of Decision Science and Technology, Tokyo Institute of Technology
3rd Author's Name Tero UUSITUPA
3rd Author's Affiliation School of Electrical Engineering, Aalto University
Date 2014-05-30
Paper # RCC2014-15,MICT2014-15
Volume (vol) vol.114
Number (no) 60
Page pp.pp.-
#Pages 6
Date of Issue