Naturalistic Enactment to Elicit and Recognize Caregiver State Anxiety

Darien Miranda, Jesus Favela, Catalina Ibarra, Netzahualcoyotl Hernandez-Cruz

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Abstract

Caring for people with dementia imposes significant stress on family members and caregivers. Often, these informal caregivers have no coping strategy to deal with these behaviors. Anxiety and stress episodes are often triggered by problematic behaviors exhibited by the person who suffers from dementia. Detecting these behaviors could help them in dealing with them and reduce caregiver burden. However, work on anxiety detection using physiological signals has mostly been done under controlled conditions. In this paper we describe an experiment aimed at inducing anxiety among caregivers of people with dementia under naturalistic conditions. We report an experiment, using the naturalistic enactment technique, in which 10 subjects were asked to care for an older adult who acts as if she experiences dementia. We record physiological signals from the participants (GSR, HR, EEG) during the sessions that lasted for approximately 30 min. We explain how we obtained ground truth from self-report and observation data. We conducted two different tests using the Support Vector Machine technique. We obtained an average precision of 77.8 % and 38.1 % recall when classifying two different possible states: “Anxious” and “Not anxious”. Analysis of the data provides evidence that the experiment elicits state anxiety and that it can be detected using wearable sensors. Furthermore, if episodes of problematic behaviors can also be detected, the recognition of anxiety in the caregiver can be improved, leading to the enactment of appropriate interventions to help caregivers cope with anxiety episodes.
Original languageEnglish
Article number192
JournalJournal of Medical Systems
Volume40
Issue number9
DOIs
Publication statusPublished - 21 Jul 2016

Keywords

  • Anxiety estimation
  • Naturalistic enactment
  • Dementia
  • Wearable sensing
  • Anxiety elicitation
  • Physiological signals

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