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Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
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Automatic Behavior Analysis During a Clinical Interview with a Virtual Human.

Albert Rizzo1, Gale Lucas1, Jonathan Gratch1

  • 1University of Southern California, Institute for Creative Technologies.

Studies in Health Technology and Informatics
|April 6, 2016
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Summary
This summary is machine-generated.

Virtual Human (VH) interviews can detect more PTSD symptoms in service members than standard health assessments. Post-deployment, VHs observed increased sadness and decreased happiness in facial expressions.

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Area of Science:

  • Human-Computer Interaction
  • Clinical Psychology
  • Behavioral Signal Processing

Background:

  • Virtual Human (VH) technology offers novel approaches for clinical interviewing and healthcare support.
  • Non-verbal behavioral signals significantly impact human-to-human communication and interaction.
  • Quantifying user emotional states through automated analysis of behavioral cues is an emerging area.

Purpose of the Study:

  • To evaluate the SimSensei platform's ability to capture and interpret real-time audiovisual behavioral signals during clinical interviews.
  • To assess if a VH interview can elicit more candid responses regarding mental health status compared to traditional assessments.
  • To analyze pre/post deployment changes in service members' emotional expressions using VH interaction data.

Main Methods:

  • Utilized the SimSensei VH interviewing platform with off-the-shelf sensors (webcams, Kinect, microphone).
  • Captured and analyzed user audiovisual behavioral signals including facial expressions, body gestures, and vocal parameters during a 20-minute interview.
  • Compared self-reported PTSD symptoms from a Post Deployment Health Assessment with symptoms revealed during the VH interview.

Main Results:

  • Service members revealed more Post-Traumatic Stress Disorder (PTSD) symptoms to the VH than reported on the standard Post Deployment Health Assessment.
  • Analysis of facial expressions indicated a higher frequency of sad expressions and a lower frequency of happy expressions post-deployment.
  • The VH system successfully inferred user states by analyzing non-verbal communication signals.

Conclusions:

  • SimSensei platform demonstrates potential for enhanced clinical interviewing by capturing nuanced behavioral data.
  • VH interviews may provide a more effective method for eliciting sensitive mental health information from service members.
  • Automated analysis of non-verbal cues can quantify user emotional states, offering valuable insights into psychological well-being, particularly in deployment-related contexts.