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Brain Imaging Investigation of the Neural Correlates of Emotion Regulation
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Towards PPG-based anger detection for emotion regulation.

Tuck-Voon How1,2, Robin E A Green3,4, Alex Mihailidis5,3

  • 1Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada. tuckvoon.how@mail.utoronto.ca.

Journal of Neuroengineering and Rehabilitation
|August 15, 2023
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Summary
This summary is machine-generated.

Wearable sensors like photoplethysmography (PPG) show potential for anger detection after traumatic brain injury (TBI). However, individual and daily variations in physiological signals present challenges for real-world emotion regulation technology.

Keywords:
Affective computingAngerContextualized rehabilitation technologyEmotionEmotion recognitionPervasive computingPhotoplethysmographyPhysiologyTraumatic brain injuryWearable

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Science

Background:

  • Anger dyscontrol is a prevalent issue following traumatic brain injury (TBI).
  • Wearable physiological sensors offer novel avenues for anger rehabilitation in daily life.
  • The efficacy of these sensors relies on accurately detecting emotional states and generalizing across real-world settings.

Purpose of the Study:

  • To investigate the utility of wearable photoplethysmography (PPG) for detecting anger in a diverse population, including individuals with TBI.
  • To explore the variability of the emotion-PPG relationship across individuals, emotional drivers, and daily changes.

Main Methods:

  • Collected the Toronto Rehabilitation Institute Emotion-Physiology (TRIEP) dataset from 32 participants (10 with TBI).
  • Utilized diverse emotional elicitation materials (film, images, self-statements, personal recall) over two sessions.
  • Performed a multi-stage analysis including time-series clustering, feature identification, and out-of-sample classification.

Main Results:

  • Photoplethysmography (PPG) signal characteristics are primarily influenced by inter-subject and intra-subject (day-to-day) variability, with emotion differentiation being secondary.
  • Both TBI and non-TBI individuals exhibited linearly separable features for anger detection within time-interval analysis.
  • The stability of these anger-specific features varied significantly across individuals and days.

Conclusions:

  • Contextual and non-stationary factors in the emotion-physiology relationship must be addressed for real-world emotion regulation technology.
  • Building robust emotion classification models necessitates a broader range of emotional data sampling.