rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Minimally-invasive wearable sensors and data processing methods for mental stress detection
  name:
    name part: Choi, Jongyoon.
    role:
      role term: creator
  name:
    name part: Gutierrez-Osuna, Ricardo
    role:
      role term: thesis advisor.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2012
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: Chronic stress is endemic to modern society. If we could monitor our mental state, we may be able to develop insights about how we respond to stress. However, it is unfeasible to continuously annotate stress levels all the time. In the studies conducted for this dissertation, a minimally-invasive wearable sensor platform and physiological data processing methods were developed to analyze a number of physiological correlates of mental stress. We present a minimally obtrusive wearable sensor system that incorporates embedded and wireless communication technologies. The system is designed such that it provides a balance between data collection and user comfort. The system records the following stress related physiological and contextual variables: heart rate variability (HRV), respiratory activity, electrodermal activity (EDA), electromyography (EMG), body acceleration, and geographical location. We assume that if the respiratory influences on HRV can be removed, the residual HRV will be more salient to stress in comparison with raw HRV. We develop three signal processing methods to separate HRV into a respiration influenced and residual HRV. The first method consists of estimating respiration-induced portion of HRV using a linear system identification method (autoregressive moving average model with exogenous inputs). The second method consists of decomposing HRV into respiration-induced principal dynamic mode and residual using nonlinear dynamics decomposition method (principal dynamic mode analysis). The third method consists of splitting HRV into respiration-induced power spectrum and residual in frequency domain using spectral weighting method. These methods were validated on a binary discrimination problem of two psychophysiological conditions: mental stress and relaxation. The linear system identification method, nonlinear dynamics decomposition method, and spectral weighting method classified stress and relaxation conditions at 85.2 %, 89.2 %, and 81.5 % respec…
  note: by Jongyoon Choi.
  note: "Major Subject: Computer Science"
  note: Electronic resource.
  note: Doctor of Philosophy Texas A&M University 2011 http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10674
  note: Includes bibliographical references.
  note: Text (Dissertation)
  subject:
    topic: Major Computer Science
  subject:
    topic: Heart Rate Variability
  subject:
    topic: Stress
  subject:
    topic: Wearable Sensors
  subject:
    topic: Electrodermal Activity
  identifier: http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10674
  record info:
    record content source: TXA
    record creation date: 121009
    record change date: 20150922122135.0
    record identifier: in00002758028