Implementation of Real-Time Body Motion Classification Using ZigBee Based Wearable BAN System

Masahiro MITTA  Minseok KIM  Yuki ICHIKAWA  

IEICE TRANSACTIONS on Communications   Vol.E103-B    No.6    pp.662-668
Publication Date: 2020/06/01
Publicized: 2020/01/10
Online ISSN: 1745-1345
DOI: 10.1587/transcom.2019HMP0005
Type of Manuscript: Special Section PAPER (Special Section on Information and Communication Technology for IoT/CPS in Medicine and Healthcare)
motion classification,  machine learning,  BAN,  radio channel,  real-time,  LQI,  

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This paper presents a real-time body motion classification system using the radio channel characteristics of a wearable body area network (BAN). We developed a custom wearable BAN radio channel measurement system by modifying an off-the-shelf ZigBee-based sensor network system, where the link quality indicator (LQI) values of the wireless links between the coordinator and four sensor nodes can be measured. After interpolating and standardizing the raw data samples in a pre-processing stage, the time-domain features are calculated, and the body motion is classified by a decision-tree based random forest machine learning algorithm which is most suitable for real-time processing. The features were carefully chosen to exclude those that exhibit the same tendency based on the mean and variance of the features to avoid overfitting. The measurements demonstrated successful real-time body motion classification and revealed the potential for practical use in various daily-life applications.