Prediction of Daily Happiness Using Supervised Learning of Multimodal Lifelog Data

Authors

  • Tetsuya Yamamoto Graduate School of Technology, Industrial and Social Sciences, Tokushima University, Tokushima, Japan; Intercontinental Neuroscience Research Group http://orcid.org/0000-0003-4241-532X
  • Junichiro Yoshimoto Graduate School of Information Science, NAIST, Nara, Japan http://orcid.org/0000-0001-7995-0321
  • Eric Murillo-Rodriguez Laboratorio de Neurociencias Moleculares e Integrativas, Escuela de Medicina, División Ciencias de la Salud, Universidad Anáhuac Mayab, Mérida, Mexico; Intercontinental Neuroscience Research Group http://orcid.org/0000-0001-9307-3783
  • Sergio Machado Laboratory of Physical Activity Neuroscience, Physical Activity Postgraduate Program, Salgado de Oliveira University, Niterói, Brazil; Intercontinental Neuroscience Research Group http://orcid.org/0000-0001-8946-8467

DOI:

https://doi.org/10.20435/pssa.v11i2.823

Abstract

Developing an approach to predict happiness based on individual conditions and actions could enable us to select daily behaviors for enhancing well-being in life. Therefore, we propose a novel approach of applying machine learning, a branch of the field of artificial intelligence, to a variety of information concerning people’s lives (i.e., a lifelog). We asked a participant (a healthy young man) to record 55 lifelog items (e.g., positive mood, negative events, sleep time etc.) in his daily life for about eight months using smartphone apps and a smartwatch. We then constructed a predictor to estimate the degree of happiness from the multimodal lifelog data using a support vector machine, which achieved 82.6% prediction accuracy. This suggests that our approach can predict the behaviors that increase individuals’ happiness in their daily lives, thereby contributing to improvement in their happiness. Future studies examining the usability and clinical applicability of this approach would benefit from a larger and more diverse sample size.

Author Biographies

Tetsuya Yamamoto, Graduate School of Technology, Industrial and Social Sciences, Tokushima University, Tokushima, Japan; Intercontinental Neuroscience Research Group

Tetsuya Yamamoto is an Associate Professor at Graduate School of Technology, Industrial and Social Sciences, Tokushima University. He is also a Clinical Psychologist and a Director of Clinical Psychoinformatics Laboratory. He received the Ph.D. degree in Human Science from the Waseda University. He completed his postdoctoral training at the University of Pittsburgh and the Hiroshima University. His academic research field is linked to vulnerability assessment and intervention for depression. Using an approach based on AI, neuroendocrinology and cognitive neuroscience (e.g., machine learning, cortisol, BDNF, EEG, fMRI), he has been conducting experiments aimed at understanding the vulnerability mechanisms for depression. 

Junichiro Yoshimoto, Graduate School of Information Science, NAIST, Nara, Japan

Junichiro Yoshimoto is an associate professor at the Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology (NAIST). He received his Ph.D. in engineering from NAIST in 2002 and worked as a researcher at the Okinawa Institute of Science and Technology until 2015. His major is machine learning and its application to data science. Recently, he is engaged in data-driven studies for depression diagnosis and subtyping using multimodal physiological signals.

Eric Murillo-Rodriguez, Laboratorio de Neurociencias Moleculares e Integrativas, Escuela de Medicina, División Ciencias de la Salud, Universidad Anáhuac Mayab, Mérida, Mexico; Intercontinental Neuroscience Research Group

Dr. Eric Murillo-Rodríguez is Psychologist and holds a PhD. in Biomedical Sciences, both degrees obtained by the National Autonomous University of Mexico (UNAM). Upon graduation, he completed his postdoctoral training at Harvard Medical School. Later, he hold an Associate Researcher position at Cellular Physiology Institute at UNAM (Mexico City, Mexico). Next, he became Full Professor at School of Medicine, Universidad Autonoma de Campeche(Campeche, Campeche. Mexico). Currently, he is a Full Professor at School of Medicine, Universidad Anahuac Mayab (Merida, Yucatan. Mexico).  His scientific contribution includes over 80 peer-reviewed publications in various reputed scientific journals, 10 chapters in books. He is an active member of several scientific societies as well as editorial board member of various journals. Dr. Murillo-Rodríguez has received outstanding academic achievements such as “Young Investigator Award” given by the World Federation of Sleep Research Societies in 2003, and subsequently the “Young Investigator Honorable Mention” awarded by the Sleep Research Society  in 2004.  Details of his publications can be viewed at ResearchGate: https://www.researchgate.net/profile/Eric_Murillo-Rodriguez

Sergio Machado, Laboratory of Physical Activity Neuroscience, Physical Activity Postgraduate Program, Salgado de Oliveira University, Niterói, Brazil; Intercontinental Neuroscience Research Group

Sergio Machado holds a degree in Physical Education from Estácio de Sá University (2005). Master, Doctorate and Postdoctoral Degrees in Mental Health by the Institute of Psychiatry (IPUB) of the Federal University of Rio de Janeiro (UFRJ) in 2008, 2012 and 2013-2014. Post-doctorate in Neurophilosophy by the Federal University of Uberlândia (IFILO / UFU) in 2012-2013 and in Neuroscience of Physical Activity by the National Institute of Translational Science and Technology in Medicine (INCT-TM) in 2014. PhD in Sports Sciences by the University of Beira Interior (UBI - Portugal) starting in 2019.1. Graduation in Psychology at Estácio de Sá University (UNESA / RJ), interrupted in 2018.1. Has experience in the areas of Mental Health, Psychophysiology, Neuropsychology, Physiology of Clinical Exercise and Neuroscience of Physical Activity. He is a Researcher at the Laboratory of Panic and Breathing of IPUB / UFRJ and Permanent Professor of the Graduate Program in Physical Activity Sciences of UNIVERSO, coordinating the Laboratory of Neuroscience of Physical Activity (LABNAF). It has as research interests the biological aspects of physical activity and physical exercise in the dimensions of prevention, rehabilitation and exercise prescription, in the perspective of health promotion and human performance. More specifically, it investigates the acute and chronic effects of strength, aerobic, flexibility and neuromotor training on brain activity, behavioral, psychophysiological, neuropsychological and quality of life aspects in healthy subjects (children / adolescents, adults and elderly) and in patients with diseases neurological disorders and psychiatric disorders. He is a guest researcher for international institutions. He is an editorial member of international magazines and a reviewer of several national and international magazines. He is currently a Young Scientist from Our State of the Foundation for Research Support of the State of Rio de Janeiro (FAPERJ)

References

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Yamamoto, T., & Yoshimoto, J. (2018, Nov). Artificial Intelligence-Based Approaches for Health Behavior Change. Association for Behavioral and Cognitive Therapies 52nd Annual Convention, Washington, DC.

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Published

2019-07-17

How to Cite

Yamamoto, T., Yoshimoto, J., Murillo-Rodriguez, E., & Machado, S. (2019). Prediction of Daily Happiness Using Supervised Learning of Multimodal Lifelog Data. Revista Psicologia E Saúde, 11(2), 145–152. https://doi.org/10.20435/pssa.v11i2.823

Issue

v. 11, n. 2, maio/ago. 2019

Section

Dossiê: "Neurociência e Saúde"

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