Towards PPG-based anger detection for emotion regulation

Journal of NeuroEngineering and Rehabilitation

Research

Tuck-Voon How¹  Alex Mihailidis¹  Robin E. A. Green² 

[1] Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada;KITE Research Institute, Toronto Rehabilitation Institute-University Health Network, Toronto, ON, Canada;KITE Research Institute, Toronto Rehabilitation Institute-University Health Network, Toronto, ON, Canada;Department of Psychiatry, University of Toronto, Toronto, ON, Canada;

关键词: Affective computing;    Anger;    Contextualized rehabilitation technology;    Emotion;    Emotion recognition;    Physiology;    Photoplethysmography;    Pervasive computing;    Traumatic brain injury;    Wearable;   

DOI  :  10.1186/s12984-023-01217-5

 received in 2022-05-11, accepted in 2023-07-10,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundAnger 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.MethodsThis 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.ResultsCharacteristics 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.ConclusionThis 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.

【 授权许可】

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

【 图 表 】

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