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Realization of wellbeing by feedback based on psychological states evaluated by objective methods

This theme aims to elucidate the relationship between the data measured in daily life and psychological states, develop technologies for objectively estimating psychological states using data from multiple sources, and develop an appropriate method of feedback according to psychological states based on scientific evidence. The goal is to realize the wellbeing of individuals by promoting their psychological state to demonstrate their abilities.

R&D Projects

<Adopted in FY2020> R&D Project TitleSummaryR&D Period
Mobile health technologies for the management of warning signs for depression toward reducing presenteeism
OKAMOTO Yasumasa (Professor, Graduate School of Biomedical and Health Sciences Department of Psychiatry and Neurosciences, Director, Center for Brain, Mind and Kansei sciences research, Hiroshima University)
Mobile health technologies for the management of warning signs for depression toward reducing presenteeism
(PDF:352KB)
2020.11-
Wellbeing augmentation via emotion sensing from facial expressions
SATO Wataru (Team Leader, Psychological Process Team, Robotics Project, RIKEN)
Wellbeing augmentation via emotion sensing from facial expressions
(PDF:169KB)
2020.11-
Mental optimization by interoception decoding and modulation
NAKAZAWA Kimitaka (Director, UTSSI, The University of Tokyo)
Mental optimization by interoception decoding and modulation
(PDF:174KB)
2020.11-
Augmenting shared dining experiences with heartfulness practice and QOL measurements
NAKAMURA Yuichi (Professor, Academic Center for Computing and Media Studies, Kyoto University)
Augmenting shared dining experiences with heartfulness practice and QOL measurements
(PDF:294KB)
2020.11-
Maximization of interventional ways for promoting health by improving fatigue debt dynamics
MIZUNO Kei (Senior Scientist, Center for Biosystems Dynamics Research, RIKEN)
Maximization of interventional ways for promoting health by improving fatigue debt dynamics
(PDF:259KB)
2020.11-

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