Worcester Polytechnic Institute Electronic Theses and Dissertations Collection

Title page for ETD etd-121912-073045


Document Typethesis
Author NameFu, Ruijun
Email Address ruijunfu at gmail.com
URNetd-121912-073045
TitleEmpirical RF Propagation Modeling of Human Body Motions for Activity Classification
DegreeMS
DepartmentElectrical & Computer Engineering
Advisors
  • Kaveh Pahlavan, Advisor
  • Yehia Massoud, Department Head
  • Kamran Sayrafian, Committee Member
  • Allen Levesque, Committee Member
  • Sergey Makarov, Committee Member
  • Keywords
  • Energy
  • Entropy
  • Backpropagation
  • Probabilistic Neural Network
  • Support Vector Machine
  • k-Nearest Neighbor
  • Averaged Fade Duration
  • Level Crossing Rate
  • Statistical Characterization
  • Body Area Networks
  • Activity Classification
  • Coherence Time
  • Doppler Spread Spectrum
  • Doppler Spread
  • RF Propagation
  • Date of Presentation/Defense2012-11-18
    Availability unrestricted

    Abstract

    Many current and future medical devices are

    wearable, using the human body as a conduit for

    wireless communication, which implies that human

    body serves as a crucial part of the transmission

    medium in body area networks (BANs). Implantable

    medical devices such as Pacemaker and Cardiac

    Defibrillators are designed to provide patients

    with timely monitoring and treatment. Endoscopy

    capsules, pH Monitors and blood pressure sensors

    are used as clinical diagnostic tools to detect

    physiological abnormalities and replace

    traditional wired medical devices. Body-mounted

    sensors need to be investigated for use in

    providing a ubiquitous monitoring environment. In

    order to better design these medical devices, it

    is important to understand the propagation

    characteristics of channels for in-body and on-

    body wireless communication in BANs. The IEEE

    802.15.6 Task Group 6 is officially working on the

    standardization of Body Area Network, including

    the channel modeling and communication protocol

    design.

    This thesis is focused on the propagation

    characteristics of human body movements.

    Specifically, standing, walking and jogging

    motions are measured, evaluated and analyzed using

    an empirical approach. Using a network analyzer,

    probabilistic models are derived for the

    communication links in the medical implant

    communication service band (MICS), the industrial

    scientific medical band (ISM) and the ultra-

    wideband (UWB) band. Statistical distributions of

    the received signal strength and second order

    statistics are presented to evaluate the link

    quality and outage performance for on-body to on-

    body communications at different antenna

    separations. The Normal distribution, Gamma

    distribution, Rayleigh distribution, Weibull

    distribution, Nakagami-m distribution, and

    Lognormal distribution are considered as potential

    models to describe the observed variation of

    received signal strength. Doppler spread in the

    frequency domain and coherence time in the time

    domain from temporal variations is analyzed to

    characterize the stability of the channels induced

    by human body movements. The shape of the Doppler

    spread spectrum is also investigated to describe

    the relationship of the power and frequency in the

    frequency domain. All these channel

    characteristics could be used in the design of

    communication protocols in BANs, as well as

    providing features to classify different human

    body activities.

    Realistic data extracted from built-in sensors in

    smart devices were used to assist in modeling and

    classification of human body movements along with

    the RF sensors. Variance, energy and frequency

    domain entropy of the data collected from

    accelerometer and orientation sensors are pre-

    processed as features to be used in machine

    learning algorithms. Activity classifiers with

    Backpropagation Network, Probabilistic Neural

    Network, k-Nearest Neighbor algorithm and Support

    Vector Machine are discussed and evaluated as

    means to discriminate human body motions. The

    detection accuracy can be improved with both RF

    and inertial sensors.

    Files
  • Fu.pdf

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