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[Preview] Shared User Interfaces of Physiological Data: Systematic Review of Social Biofeedback Systems and Contexts in HCI
Clara Moge, Katherine Wang, Youngjun Cho

CHI'22: ACM Conference on Human Factors in Computing Systems
Session: Communities

Abstract
As an emerging interaction paradigm, physiological computing is increasingly being used to both measure and feed back information about our internal psychophysiological states. While most applications of physiological computing are designed for individual use, recent research has explored how biofeedback can be socially shared between multiple users to augment human-human communication. Reflecting on the empirical progress in this area of study, this paper presents a systematic review of 64 studies to characterize the interaction contexts and effects of social biofeedback systems. Our findings highlight the importance of physio-temporal and social contextual factors surrounding physiological data sharing as well as how it can promote social-emotional competences on three different levels: intrapersonal, interpersonal, and task-focused. We also present the Social Biofeedback Interactions framework to articulate the current physiological-social interaction space. We use this to frame our discussion of the implications and ethical considerations for future research and design of social biofeedback interfaces.

WEB:: http://programs.sigchi.org/chi/2022/p...
Presentation Video::    • Shared User Interfaces of Physiological Da...  
DOI:: https://doi.org/10.1145/3491102.3517495
Video previews for CHI 2022 papers