AAAI Technical Report SS-12-05
Self-Tracking and Collective Intelligence for Personal Wellness
Self-Tracking Mindfulness Incorporating a Personal Genome
Takashi Kidoa,b,c
a
RikenGenesis Co., Ltd., Taito-ku, Taito, 1-5-1, Tokyo, Japan.
Stanford University School of Medicine, 1265 Welch Road, Stanford, CA, 94305-5479.
c
Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology Agency (JST).
b
takashi.kido@rikengenesis.jp
personal genome applications for medical purposes have
been widely discussed, there has been little discussion on
its applications for other purposes (Inoue and Muto, 2011).
In this paper, we discuss the possibility of designing new
personal genome applications using QS technologies in the
non-medical domain of mental performance. We also
propose the concept of the MyFinder project and discuss
the ongoing challenges regarding the application of the
personal genome in non-medical domains.
Abstract
This paper introduces the ongoing MyFinder project, which
was launched in October 2010. The goals of this project are:
(1) to propose an intimate personal genome information
environment, MyFinder, which supports the search for our
inborn talents and maximizes our potential for a meaningful
life; and (2) to contribute to scientific discoveries in the
biomedical or psychological research domains through
intelligent community computing, or in other words, citizen
science. This paper describes our research framework and
the ongoing challenges to achieving these two goals. We
also discuss the technical and social issues related to
possible personal genome applications in non-medical
domains.
The Concept of MyFinder
The MyFinder project is a novel research framework for
the personal genome environment. It combines the notion
of an intelligent agent in the Artificial Intelligence (AI)
community and personal genome research in the
biomedical community (Kido, 2011). The concept of
MyFinder is represented visually in Figure 1. While
traditional personal genome services primarily aim to
establish personalized medicine, such as disease treatment
or drug response, MyFinder focuses more on wellness,
psychiatry, and behavioral sciences.
The MyFinder project has two goals. The first is to
provide a personal genome environment that enables us to
find our inborn nature, strengths, and talents, and to
maximize our potential. The second is to provide a
research platform for scientific discovery with community
computing.
By observing and analyzing our daily behaviors, such as
diet, sleep, working style, time management, social
interactions, and individual preferences, we can monitor
our daily physical, chemical, and mental stress levels.
Quantified Self (QS) technologies and intelligent software
agent technologies may be useful for learning a user’s
behavioral trends and analyzing stress states.
Recent studies have suggested that individual mental
performance and psychological recognition may be
influenced by genetic factors. More interestingly, the
influence of genetic factors is not deterministic, as genetic
activity may be controlled by environmental factors, such
as physical, chemical, and psychological stress levels. For
example, some recent studies have reported that laughing
is very effective in the treatment of type 2 diabetes
(Hayashi, 2006), which suggests that the positive mental
Introduction
The Quantified Self (QS) community, in which people
inspire one another to share ideas on self-tracking systems
for sleep, activity, location, heart rate, facial expressions,
mood, diet, productivity, and cognition, has recently
emerged (Quantified Self, 2011). Conventional health care
records were thought to be useful only to physicians and
researchers. However, the QS movement may change the
notion of conventional health care, in the same way as the
emergence of personal computers changed the traditional
notion of computers. Although biomedical scientists have
attempted to build a database of human traits and disease to
realize data-driven personalized medicine (Butte, 2008),
non-medical applications for healthy people are rarely
discussed.
A particularly ambitious movement in the biomedical
community is the personal genome. The price for obtaining
one’s personal genome sequence – a comprehensive map
of the 3 billon bases in our DNA – has been falling rapidly,
from $2 billion in 2000 to around $1,000 within the next
few years (Davies, 2010). Personal genome sequencing
and its medical interpretation will play very important roles
in realizing personalized medicine (Ashley, 2010). While
Copyright © 2012, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.
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state induced by laughter activates genetic triggers of
cellular activity.
By incorporating such observations with personal
genome data and self-tracked data of individual daily
behaviors, MyFinder may learn a user’s behavioral trends
(for example, learning by a psychological theory, such as
the enneagram theory) and suggest some findings for users
so that the user’s unique behavior trend could be
influenced by his/her inborn genetic variance. This finding
may be valuable for users to rediscover their inborn
potential and for the scientific community to share new
scientific evidence.
Important questions in this concept are: (1) How can we
design the scheme to discover these findings? and (2) How
do people feel about these findings? To answer these
questions, we must understand what people would really
want to do with their personal genome.
mindfulness meditation training has also been investigated
(Hudlicka, 2011). Brain wave and heart wave analyses may
be useful for characterizing an individual’s mental status.
Brain training research such as Lumosity (Lumosity, 2011)
or Compassion and Altruism Research (CCARE, 2011)
may provide good tools and platforms for designing
mindfulness technologies. We are currently developing a
cloud computing environment for the self-tracking of brain
wave and heart wave data.
(2) Personal Genome Interpretation
While the costs associated with obtaining genetic
information have fallen sharply, the medical interpretation
of genetic risk remains unclear (Ashley, 2011). Research
into the medical interpretation of personal genome data is
currently a very hot topic in genetics. In addition to
medical interpretations, personal genome interpretation for
psychological resources is also an interesting and
important challenge. For example, genetic associations
have recently been reported between the oxytocin receptor
single-nucleotide
polymorphism
rs53576
and
psychological resources such as optimism, mastery, and
self-esteem (Saphire-Bernstein, 2011). This genetic
variation may influence social behavior and personality.
Approaches based on brain science and neuroimaging
technologies integrated with a personal genome may
therefore be useful for understanding the diversity of
human cognition and behavior. However, further
investigation is necessary.
(3) Behavior Pattern Analyses on Human
Attitude
It is important to understand how a new personal genome
application can be designed to influence and change human
behavior. Investigating how knowledge of genetic risk
affects people is a major challenge. Social psychological
experiments may be useful for understanding various
attitudes (Elliot, 2003). Learning about human attitudes in
various cultural contexts and classifying the genetic
knowledge with several measures (such as credibility,
harmfulness, seriousness, usefulness, and interest factors)
will be useful for designing intimate and persuasive
interactive computer systems.
Figure 1: The Concept of MyFinder
MyFinder provides a personal genome environment that
enables us to find our inborn nature, strengths, and talents,
and to maximize our potential. MyFinder also provides a
research platform for scientific discovery with community
computing.
Challenge Problems
Self-Tracking of Mental Status in Daily Activity
We are designing and implementing a daily self-tracking
environment for monitoring individual mental status with
daily event logs. The first challenge is to investigate what
kind of data would be useful for understanding how people
feel about their daily experience and how we can
effectively collect the data in intimate and persuasive
ways.
(1) Design Mindfulness Technologies
One of the major challenges is the self-tracking of
individual mental performance. Mindfulness, a form of
Buddhist meditation, is a good example of mental
performance monitoring. Modern clinical psychology and
psychiatry have developed a number of therapeutic
applications based on the concept of mindfulness
(Kabat-Zinn, 1990). Virtual coaching to provide
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We came up with ideas on mental state tracking in
everyday life from the “flow theory” proposed by
Csikszentmihalyi (1998). Csikszentmihalyi developed the
“Experience Sampling Method” (ESM) to determine how
people actually spend their ordinary days. For the ESM,
the participants of a study are required to report what they
are doing and how they are feeling whenever they receive a
pocket pager’s signal, at random intervals, eight times a
day for a week. By statistically analyzing ESM responses,
researchers try to measure “flow” (in other words, “zone”)
state. The “flow” state is defined as a mental state of
operation in which a person in an activity is fully immersed
in a feeling of energized focus, fully involved, and
successfully performing the activity.
our personal mental profile, (Figure 3), we may learn how
to be more mindful of our psychological state.
Our challenge is to design a new ESM environment that
incorporates new mobile health technology. Recent
innovations in portable devices for brain wave and heart
rate detection may provide a new objective definition of
the “flow” state in addition to the traditional subjective
self-reported ESM description.
Figure 2A: Self-tracking the emotional states associated
with daily events. The user chooses the visual symbols in
each category (Food, Relationships, Work, and Health) for
self-tracking the user’s emotional states or physical
conditions. Each symbol represents the user’s mental or
physical conditions in several daily events. The user can
easily choose the appropriate visual symbols (with short
comments, if necessary) and quickly upload them to the
server.
Current System Design and Implementation
We have been developing a self-tracking environment for
collecting data on a person’s daily events and mental status.
Some example images of the tracking and displaying of
daily events data are shown in Figure 2A and Figure 2B.
We designed a system in which users can easily self-track
their daily events and their emotional feelings by choosing
predefined visual symbols (Fig. 2A). If necessary, the user
can upload some short text messages to trace what events
caused the feelings. The system allows connection of the
portable brain wave sensors developed by Neurosky Inc.,
and heart wave sensors developed by HeartMath.
Accumulated symbols, messages, and sensory data can be
displayed in daily, weekly, and monthly aggregates.
We expect users to have some sense of new awareness by
observing the time series of their self-tracking records and
to build up their own subtle habits for self-discovery.
Collected symbols, messages, and sensory data can be
analyzed and correlated. The results are expected to
provide good resources for advancements in the social
sciences and wellness medicine.
Figure 2B: Quantified Self-User Event Timeline. The
daily self-tracking data can be visualized in a timeline
format. Self-tracking data may include visual symbols of
daily events, short comments, pictures, and stress status
(relaxed or focused) data captured by devices that record
brain waves and heart rate. The user can trace back his/her
mental or physical conditions in daily, weekly, and
monthly aggregates.
Figure 3 shows an example of self-tracking history of
brainwave signals with NeuroSky’s sensor. Time series of
brainwave signals are recorded with personal actions. We
expect to discover some unique relationships between
signal patterns and actions. Observing how the actions
influence the brainwave patterns demonstrates how actions
potentially influence our mental activity. By recognizing
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Figure 3: An example of self-tracking history of brainwave signals with NeuroSky’s sensor. The X-axis
shows the time (minutes) and the Y-axis shows the brainwave signal intensity defined by NeuroSky’s algorithm. The
red line indicates “Attention” signal and blue line indicates “Meditation” signal calculated by NeuroSky’s algorithm.
Three events, (A) talking on the phone, (B) working on the computer and (C) playing a video game, are labeled. In
this example, playing a video game increases the both of “Attention” and “Meditation” levels.
Table 1: Genes and SNPs that have previously been reported for cognitive or psychological phenotypes.
CANTAB, Cambridge Neuropsychological Test Automated Battery.
L&M, learning and memory.
34
How can we design a scheme to discover our unique
potential?
We have been designing a scheme for supporting
self-discovery processes in two ways. The first way is the
Quantified Self (QS) approach. We assume that
self-tracking our daily events with emotions may be useful
for discovering our unique trend of behavior patterns,
feeling patterns, and thinking patterns. Our technical
challenges are how to collect various data in an easy way,
how to find the essential variables, and how to analyze data
for finding meaningful patterns. We are currently
investigating what kind of data collection would be useful
for the self-discovery process; for example, self-tracking
data may include a diary, social network interactions,
self-reporting messages, diagnosis type questionnaires, and
body sensors to measure physical parameters such as heart
rate and brain wave pattern. We expect to find some
statistical correlations between subjective variables and
objective ones, but some interventions may be necessary to
prove their causal relationships.
Personal Genome for Understanding Human
Mind
Understanding how our human mind evolved remains a
challenge. Minsky (2006) proposed the concept of an
emotion machine, offering a new model for how our minds
work. Minsky argues persuasively that emotion, intuition,
and feeling are not distinct things, but different ways of
thinking. We are interested in understanding how various
ways of thinking evolve how complex diversity of
personalities emerges.
Mental performance genomics is an important new area of
research and many links between personal genetic profiles
and mental performance have been recently reported. Table
1 shows the genes and SNPs that have previously reported
associations with cognitive or psychological phenotypes.
Recent papers report some genetic variances associated
with optimism and empathy, extraversion, and altruism
(Saphire-Bernstein, 2011). We have been collaborating
with DIYgenomics (DIYgenomics, 2011) and are planning
to conduct a social intelligence study assessing
genotype-phenotype linkage in optimism, empathy,
extraversion, and altruism.
The second way is a citizen science approach, in which
non-expert volunteers are willing to share their data and
participate in community computing projects for scientific
discovery. We have been designing citizen science genetics
projects for social intelligence research in which we test a
hypothesis of whether some type of genetic variances are
significantly correlated with mental performance traits of
social intelligence, such as optimism, empathy,
extraversion, and altruism.
Our hypothesis is that individuals with certain genetic
profiles may have greater natural capacity for
characteristics of social intelligence. Recognized standard
online survey instruments can also be prepared for the
phenotypic assessments of these mental traits. We will
investigate whether the findings of previous studies are
replicated in our citizen science cohort and then explore
the potential causes and influences of these social
intelligence traits. We will also investigate whether social
intelligence skills may be improved through intervention
via mobile application and other methods.
In both the QS and citizen science approaches, the key
success factor is the synergy of science, technology, and
social movement. Science success is new discovery,
technological success is new technological innovation, and
social movement success is motivation of people. These
challenges open up new interdisciplinary research
paradigms in science, technology, and society.
Discussion
How do people feel about new findings on themselves?
We have discussed the methodologies for finding our
unique potential and describe technological and scientific
challenges in QS and citizen science genetic approaches.
Another important question is how people feel when they
know their inborn genetic destiny. This topic is more
related to social psychology than to technology. Even if
genetic factors are not deterministic and environment
factors have larger influences than genetic factors, some
people may have a concern about knowing their innate
strengths and weaknesses. Cultural differences may also
affect people’s attitude on knowing their inborn talents or
flaws. We will continue to investigate these topics by
conducting behavior pattern analyses on people’s attitudes.
We have described our goals, concepts, challenges, system
designs, and study designs for our ongoing project,
MyFinder. While the mainstream goal of personal genome
application is considered to be the realization of personal
medicine, we focus much more attention on other
possibilities in non-medical domains. We have been trying
to design self-tracking environments for capturing our
mental status with our daily events, in subjective and
objective ways. We then try to incorporate these
self-tracking data, especially mental performance data,
with the personal genome.
Our ongoing challenges pose new types of questions on
how to live our own life, which open up new
interdisciplinary research paradigms in science, technology,
and society.
35
[14] The Center for Compassion and Altruism Research
and Education (CCARE). http://ccare.stanford.edu/,
2011.
[15] Inoue, Y, and Muto, K. "Children and genetic
identification of talent." The Hastings Center Report
41, no. 5, 2011.
[16] Csikszentmihalyi, M. Finding Flow: The
Psychology of Engagement with Everyday Life
(Masterminds Series) , Basic Books, 1998.
[17] Minsky, M. The Emotion Machine: Commonsense
Thinking, Artificail Intelligence,and the Future of the
Human Mind, Simon & Schuster, 2006.
[18] Need et. al, A genome-wide study of common SNPs
and CNVs in cognitive performance in the CANTAB,
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[19] Kogan A, Saslow LR, Impett EA, Oveis C, Keltner D,
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[20] Rodrigues SM, Saslow LR, Garcia N, John OP,
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Summary
In this paper, we introduced the ongoing MyFinder project
and described three significant challenges. We also
introduced our system for the self-tracking of mental
performance, along with our personal genome approach for
understanding the human mind.
Acknowledgement
The project described was supported by a grant from the
JST Sakigake program. I thank the following individuals:
Melanie Swan, a founder of DIYgenomics for citizen
science collaboration; Atul J. Butte and Keiichi Kodama,
Stanford University School of Medicine; Jin Yamanaka, an
independent software creator; and Minae Kawashima,
Graduate School of Medicine, Tokyo University.
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