Human Motion Classification Using Radio Signal Strength in WBAN

Sukhumarn ARCHASANTISUK; Takahiro AOYAGI; Tero UUSITUPA; Minseok KIM; Jun-ichi TAKADA

doi:10.1587/transcom.2015MIP0009

Human Motion Classification Using Radio Signal Strength in WBAN

Sukhumarn ARCHASANTISUK, Takahiro AOYAGI, Tero UUSITUPA, Minseok KIM, Jun-ichi TAKADA

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In this paper, a novel approach of a human motion classification system in wireless body area network (WBAN) using received radio signal strength was developed. This method enables us to classify human motions in WBAN using only the radio signal strength during communication without additional tools such as an accelerometer. The proposed human motion classification system has a potential to be used for improving communication quality in WBAN as well as recording daily-life activities for self-awareness tool. To construct the classification system, a numerical simulation was used to generate WBAN propagation channel in various motions at frequency band of 403.5MHz and 2.45GHz. In the classification system, a feature vector representing a characteristic of human motions was computed from time-series received signal levels. The proposed human motion classification using the radio signal strength based on WBAN simulation can classify 3-5 human motions with the accuracy rate of 63.8-95.7 percent, and it can classify the human motions regardless of frequency band. In order to confirm that the human motion classification using radio signal strength can be used in practice, the applicability of the classification system was evaluated by WBAN measurement data.

Publication: IEICE TRANSACTIONS on Communications Vol.E99-B No.3 pp.592-601

Publication Date: 2016/03/01

Publicized

Online ISSN: 1745-1345

DOI: 10.1587/transcom.2015MIP0009

Type of Manuscript: Special Section PAPER (Special Section on Information and Communication Technology for Healthcare and Medical Applications in Conjunction with Main Topics of ISMICT2015)

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Authors

Sukhumarn ARCHASANTISUK
  Tokyo Institute of Technology
Takahiro AOYAGI
  Tokyo Institute of Technology
Tero UUSITUPA
  Niigata University
Minseok KIM
  Tokyo Institute of Technology
Jun-ichi TAKADA

Keyword

human motions, classification, radio signal strength, WBAN, simulation

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Sukhumarn ARCHASANTISUK, Takahiro AOYAGI, Tero UUSITUPA, Minseok KIM, Jun-ichi TAKADA, "Human Motion Classification Using Radio Signal Strength in WBAN" in IEICE TRANSACTIONS on Communications, vol. E99-B, no. 3, pp. 592-601, March 2016, doi: 10.1587/transcom.2015MIP0009.
Abstract: In this paper, a novel approach of a human motion classification system in wireless body area network (WBAN) using received radio signal strength was developed. This method enables us to classify human motions in WBAN using only the radio signal strength during communication without additional tools such as an accelerometer. The proposed human motion classification system has a potential to be used for improving communication quality in WBAN as well as recording daily-life activities for self-awareness tool. To construct the classification system, a numerical simulation was used to generate WBAN propagation channel in various motions at frequency band of 403.5MHz and 2.45GHz. In the classification system, a feature vector representing a characteristic of human motions was computed from time-series received signal levels. The proposed human motion classification using the radio signal strength based on WBAN simulation can classify 3-5 human motions with the accuracy rate of 63.8-95.7 percent, and it can classify the human motions regardless of frequency band. In order to confirm that the human motion classification using radio signal strength can be used in practice, the applicability of the classification system was evaluated by WBAN measurement data.
URL: https://globals.ieice.org/en_transactions/communications/10.1587/transcom.2015MIP0009/_p

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@ARTICLE{e99-b_3_592,
author={Sukhumarn ARCHASANTISUK, Takahiro AOYAGI, Tero UUSITUPA, Minseok KIM, Jun-ichi TAKADA, },
journal={IEICE TRANSACTIONS on Communications},
title={Human Motion Classification Using Radio Signal Strength in WBAN},
year={2016},
volume={E99-B},
number={3},
pages={592-601},
abstract={In this paper, a novel approach of a human motion classification system in wireless body area network (WBAN) using received radio signal strength was developed. This method enables us to classify human motions in WBAN using only the radio signal strength during communication without additional tools such as an accelerometer. The proposed human motion classification system has a potential to be used for improving communication quality in WBAN as well as recording daily-life activities for self-awareness tool. To construct the classification system, a numerical simulation was used to generate WBAN propagation channel in various motions at frequency band of 403.5MHz and 2.45GHz. In the classification system, a feature vector representing a characteristic of human motions was computed from time-series received signal levels. The proposed human motion classification using the radio signal strength based on WBAN simulation can classify 3-5 human motions with the accuracy rate of 63.8-95.7 percent, and it can classify the human motions regardless of frequency band. In order to confirm that the human motion classification using radio signal strength can be used in practice, the applicability of the classification system was evaluated by WBAN measurement data.},
keywords={},
doi={10.1587/transcom.2015MIP0009},
ISSN={1745-1345},
month={March},}

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TY - JOUR
TI - Human Motion Classification Using Radio Signal Strength in WBAN
T2 - IEICE TRANSACTIONS on Communications
SP - 592
EP - 601
AU - Sukhumarn ARCHASANTISUK
AU - Takahiro AOYAGI
AU - Tero UUSITUPA
AU - Minseok KIM
AU - Jun-ichi TAKADA
PY - 2016
DO - 10.1587/transcom.2015MIP0009
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E99-B
IS - 3
JA - IEICE TRANSACTIONS on Communications
Y1 - March 2016
AB - In this paper, a novel approach of a human motion classification system in wireless body area network (WBAN) using received radio signal strength was developed. This method enables us to classify human motions in WBAN using only the radio signal strength during communication without additional tools such as an accelerometer. The proposed human motion classification system has a potential to be used for improving communication quality in WBAN as well as recording daily-life activities for self-awareness tool. To construct the classification system, a numerical simulation was used to generate WBAN propagation channel in various motions at frequency band of 403.5MHz and 2.45GHz. In the classification system, a feature vector representing a characteristic of human motions was computed from time-series received signal levels. The proposed human motion classification using the radio signal strength based on WBAN simulation can classify 3-5 human motions with the accuracy rate of 63.8-95.7 percent, and it can classify the human motions regardless of frequency band. In order to confirm that the human motion classification using radio signal strength can be used in practice, the applicability of the classification system was evaluated by WBAN measurement data.
ER -