Towards PPG-based anger detection for emotion regulation.

Humans Emotional Regulation Photoplethysmography Anger / physiology Emotions / physiology Brain Injuries, Traumatic
Affective computing Anger Contextualized rehabilitation technology Emotion Emotion recognition Pervasive computing Photoplethysmography Physiology Traumatic brain injury Wearable

Journal

Journal of neuroengineering and rehabilitation
ISSN: 1743-0003
Titre abrégé: J Neuroeng Rehabil
Pays: England
ID NLM: 101232233

Informations de publication

Date de publication:
15 08 2023
Historique:
received: 11 05 2022
accepted: 10 07 2023
medline: 17 8 2023
pubmed: 16 8 2023
entrez: 15 8 2023
Statut: epublish

Résumé

Anger dyscontrol is a common issue after traumatic brain injury (TBI). With the growth of wearable physiological sensors, there is new potential to facilitate the rehabilitation of such anger in the context of daily life. This potential, however, depends on how well physiological markers can distinguish changing emotional states and for such markers to generalize to real-world settings. Our study explores how wearable photoplethysmography (PPG), one of the most widely available physiological sensors, could be used detect anger within a heterogeneous population. This study collected the TRIEP (Toronto Rehabilitation Institute Emotion-Physiology) dataset, which comprised of 32 individuals (10 TBI), exposed to a variety of elicitation material (film, pictures, self-statements, personal recall), over two day sessions. This complex dataset allowed for exploration into how the emotion-PPG relationship varied over changes in individuals, endogenous/exogenous drivers of emotion, and day-to-day differences. A multi-stage analysis was conducted looking at: (1) times-series visual clustering, (2) discriminative time-interval features of anger, and (3) out-of-sample anger classification. Characteristics of PPG are largely dominated by inter-subject (between individuals) differences first, then intra-subject (day-to-day) changes, before differentiation into emotion. Both TBI and non-TBI individuals showed evidence of linear separable features that could differentiate anger from non-anger classes within time-interval analysis. However, what is more challenging is that these separable features for anger have various degrees of stability across individuals and days. This work highlights how there are contextual, non-stationary challenges to the emotion-physiology relationship that must be accounted for before emotion regulation technology can perform in real-world scenarios. It also affirms the need for a larger breadth of emotional sampling when building classification models.

Sections du résumé

BACKGROUND
Anger dyscontrol is a common issue after traumatic brain injury (TBI). With the growth of wearable physiological sensors, there is new potential to facilitate the rehabilitation of such anger in the context of daily life. This potential, however, depends on how well physiological markers can distinguish changing emotional states and for such markers to generalize to real-world settings. Our study explores how wearable photoplethysmography (PPG), one of the most widely available physiological sensors, could be used detect anger within a heterogeneous population.
METHODS
This study collected the TRIEP (Toronto Rehabilitation Institute Emotion-Physiology) dataset, which comprised of 32 individuals (10 TBI), exposed to a variety of elicitation material (film, pictures, self-statements, personal recall), over two day sessions. This complex dataset allowed for exploration into how the emotion-PPG relationship varied over changes in individuals, endogenous/exogenous drivers of emotion, and day-to-day differences. A multi-stage analysis was conducted looking at: (1) times-series visual clustering, (2) discriminative time-interval features of anger, and (3) out-of-sample anger classification.
RESULTS
Characteristics of PPG are largely dominated by inter-subject (between individuals) differences first, then intra-subject (day-to-day) changes, before differentiation into emotion. Both TBI and non-TBI individuals showed evidence of linear separable features that could differentiate anger from non-anger classes within time-interval analysis. However, what is more challenging is that these separable features for anger have various degrees of stability across individuals and days.
CONCLUSION
This work highlights how there are contextual, non-stationary challenges to the emotion-physiology relationship that must be accounted for before emotion regulation technology can perform in real-world scenarios. It also affirms the need for a larger breadth of emotional sampling when building classification models.

Identifiants

DOI: 10.1186/s12984-023-01217-5 PMID: 37582733 PMC: PMC10426222
pubmed: 37582733
doi: 10.1186/s12984-023-01217-5
pii: 10.1186/s12984-023-01217-5
pmc: PMC10426222
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

107

Subventions

Organisme : CIHR
Pays : Canada

Informations de copyright

© 2023. BioMed Central Ltd., part of Springer Nature.

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Auteurs

Tuck-Voon How (TV)

Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada. tuckvoon.how@mail.utoronto.ca.
KITE Research Institute, Toronto Rehabilitation Institute-University Health Network, Toronto, ON, Canada. tuckvoon.how@mail.utoronto.ca.

Robin E A Green (REA)

KITE Research Institute, Toronto Rehabilitation Institute-University Health Network, Toronto, ON, Canada.
Department of Psychiatry, University of Toronto, Toronto, ON, Canada.

Alex Mihailidis (A)

Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
KITE Research Institute, Toronto Rehabilitation Institute-University Health Network, Toronto, ON, Canada.

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