Running head: PERVASIVE MENTAL HEALTH !1 · 2018. 10. 11. · PERVASIVE MENTAL HEALTH !3 Pervasive Mental Health Health and fitness wearables are increasing in popularity. IndustryARC,

Running head: PERVASIVE MENTAL HEALTH !1

!!!!!!!!!!Pervasive Mental Health

Christine Meinders

Art Center College of Design

!!!!!!!!!!!!!!!!!

PERVASIVE MENTAL HEALTH !2

Abstract

This paper explores the connection between pervasive computing and mental health by

examining current research in pervasive computing, big data and mental health diagnostic

systems. Additionally, the paper questions our current conceptions and utilizations of the internet

of things (IoT), data and diagnosis, and recommends a restructuring of how we view these topics.

Finally, the paper concludes with an exploration of the future of mental health, pervasive

computing, brain-computer interfaces, and the implications these have for designing from a

wired mental health perspective.

Keywords: ubiquitous computing, pervasive computing, everyware, internet of things,

IoT, werables, mental health, diagnostic and statistical manual of mental disorders, DSM, affect,

big data, privacy, smart sensors, diagnosis, brain-computer interface, neuroprosthetics, cortical

modem

!!!!!!!!!!!!!!!!!!!


Pervasive Mental Health

Health and fitness wearables are increasing in popularity. IndustryARC, a market

research company based in India “…expects wearable medical devices to ring up $41.3 billion in

sales by 2020.” (Hernandez, 2015) Global analytics company CCS Insight predicts even more

activity from wearable technologies, projecting “Sales of wearables will grow from 29 million in

2014 to 172 million in 2018, with a spike in 2015. The most popular devices will be

smartwatches and fitness trackers” (CCS Insight, 2015). Additionally, with devices like the

Apple Watch and numerous Android and other wearables, the functionality of our smart phones

and independent fitness trackers are merging. This could have a profound and immediate effect

on the health industry and eventually the mental health industry.

Currently, there are very few wearables for mental health. The majority of the wearables

on the market today are focused on physical health, collecting metrics like heart rate,

perspiration, and number of steps taken daily. However, one wearable called “The Pavlok” is a

wrist wearable that relies on Pavlovian conditioning via an electric shock to modify unwanted

behaviors like spending too much time on the internet, or will “shame” you by posting to your

social media sites if you fail to make it to the gym (indiegogo Pavlok website, 2015). Another

wearable which has the potential to modify behavior is a recent project by Kristina Orgeta and

Jenny Rodenhouse and their creation of “sensor extensions” for fingernails. At a presentation at

Art Center (Rodenhouse & Ortega, 2014), one suggested use of these wearable fingernail-based

sensors was an electric shock to assist with smoking cessation.

Wearables can be individualized tools that can help us to better understand how we

diagnose mental health disorders. This paper will explore existing weaknesses in our current


diagnostic system, misconceptions about privacy and big data, and will offer an alternative

perspective to our current ideas about pervasive computing from a mental health system

perspective. It will also explore the layout and design of this mental health systems perspective

and its current and future applications.

Diagnosing

Background

“The Diagnostic and Statistical Manual of Mental Disorders (DSM) is the standard

classification of mental disorders used by mental health professionals in the United States. (APA-

DSM, 2015) This diagnostic tool is based on theory, biology and genetics and combines

empirical evidence with inter-rater reliability. Not only is the DSM the primary mental health

diagnostic tool here in the United States, it is the primary diagnostic tool around the world, as the

“DSM-5 contains all of the information needed to assign HIPAA-compliant, valid ICD-10-CM

codes to the psychiatric diagnoses … for patients.” (APA-DSM, 2015) The ICD-10 stands for

International Statistical Classification of Diseases and Related Health Problems, and is a medical

classification system used by the World Health Organization. Understanding how we diagnose

disorders, as well as how our concepts of disorders are “exported” worldwide, affects how

people view “disorders” and “abnormality” throughout the world.

Issues

The development of each iteration of the DSM has not been without controversy.

Theoretical and cultural perspectives and biases, influence from the pharmaceutical industry, and

the basic approach to classification have been major issues throughout the history of the DSM.

The DSM-III was a turning point in the history of the DSM, as it dropped the psychodynamic


perspective and focused on an empirical research based approach with an increased focus on

genetics and biology in the diagnostic categories. This shift in perspective opened the door for

the pharmaceutical industry to create drugs for these new diagnostic categories and criteria.

(Tartakovsky, 2011) The same pharmaceutical connections that emerged from the development

of the DSM-III are an increasing concern today. The DSM-5 task force required contributors to

sign a confidentiality agreement and “It’s been pointed out that about 70% of current task force

members have ties to the pharmaceutical industry, up about 14% from DSM-IV.” (Collier, 2010 )

The DSM-IV was also under scrutiny. “Of the 170 DSM panel members 95 (56%) had one or

more financial associations with companies in the pharmaceutical industry. One hundred percent

of the members of the panels on 'Mood Disorders' and 'Schizophrenia and Other Psychotic

Disorders' had financial ties to drug companies.” (Cosgrove, Krimsky, Vijayaraghavan and

Schneider, 2006) According to a 2005 National Institute of Health (NIH) study, “Major

depressive disorder is, by itself, the leading cause of disability among Americans age 15 – 44,

according to the World Health Organization”. (NIH fact sheet, accessed April, 2015)

Cultural differences have been another major consideration over the past few decades in

the development of the DSM. One of the main issues with including or not considering culture in

the DSM, is in order to have a view on culture; you must consider whether disorders are rooted

in biology or experience. In a Boston Globe article, Latif Nasser (2012) writes, “If underlying

mental illness is universal, then what looks like a “culture-bound syndrome” is likely to be a

common problem that happens to show up differently in different settings.” However, Nasser

presents the counter-perspective, “Culture doesn’t just shape what a mentally ill person calls his

or her illness, they argue  —  it determines what counts as illness in the first place.” (Nasser, 2012)


This helps to frame the debate regarding the inclusion of “Culture-Bound Syndromes” in the

DSM-IV-TR. Culture Bound Syndromes are comprised of psychiatric and somatic symptoms

within a specific culture. The DSM-IV-TR tries to address culture in the Glossary of Culture-

Bound Syndromes and an Outline for Cultural Formation which is meant to “…supplement the

multi axial diagnostic assessment and to address difficulties that may be encountered in apply the

DSM-IV criteria in a multicultural environment.” (DSM–IV–TR (2000) 4th ed., text rev., pg. 897)

The problem with this approach is that is favors Western perspectives as the norm and as Nasser

suggests, doesn’t look at culture as the source of the “disorder”. The DSM-5 tried to re-conceive

Culture Bound Syndromes by making dramatic changes which includes a section on the Cultural

Formulation Interview (CFI) and Cultural Concepts of Distress. This re-evaluation of distress is

recognized as being locally shaped. (DSM-5 (2013), pg. 758) Psychocultural researcher

Constance A. Cummings summarizes talks that were given at the annual meeting of the Society

for the Study of Psychiatry and Culture on May 3–5 in Toronto outlining the changes (in the

DSM-5). She describes a comment from the end of the meeting from cultural psychiatrist

Laurence Kirmayer:

The DSM-5’s cultural revisions challenge “the fundamental logic of psychiatric nosology”– i.e.,

“describing problems located inside peoples’ heads” – which is at odds not only with the fact that “when

we start talking about languages and suffering . . . we are embedded in social networks and interpersonal

relations and local worlds,” but with new work in social and cultural neuroscience and social genomics that

illuminates how social factors, like childhood adversity, social isolation, migration, and stigma, affect

mental health and illness. (Cummings, 2013)

While these are significant advances to how culture is viewed in the context of mental

health, the primary concern should lie in the conflicts of interest within the DSM task force and

https://thefprorg.wordpress.com/author/thefprorg/

http://www.psychiatryandculture.org/SSPC2013.htm

http://www.mcgill.ca/tcpsych/faculty/laurencekirmayer


the APA, and how this effects the creation and revision of diagnostic categories and criteria.

Additionally, the APA’s profits from the publication of the DSM itself should be viewed as an

additional conflict of interest as “dependence on the publishing profits generated by the DSM 5

business enterprise… is an inherent and influential conflict of interest between the DSM 5 public

trust and DSM 5 as a best seller.” (Frances, 2012)

Jeffrey Poland, Barbara Von Eckardt and Will Spaulding identified issues with the very

approach to classifying Psychopathology within the DSM, “Our view is that the DSM constitutes

a faulty conceptualization of the domain of psychopathology and that it interferes with optimal

pursuit of clinical and scientific purposes…One reason for this ineffectiveness is the absence of a

well-developed alternative which can play the roles currently served by the DSM.” (Poland, Von

Eckardt & Spaulding, 1994) The very categorization is a concern, especially when we consider

how this translates into insurance coverage, medical and other supportive benefits. This

perspective is important to consider when harnessing big data and ubiquitous computing.

Why is the DSM important?

It is important to understand the current state of our global mental health diagnostic

system so we can understand how to use elements from business and technology to try to

“correct” this corruption. What are the ramifications of a corrupt diagnostic system? For

example, during the development of the DSM-III there was an increase in diagnosing childhood

bipolar disorder (Sterbenz, 2013). There was an over-diagnosing and over-medication of

disorders like Attention Deficit Hyperactivity Disorder (ADHD), and in the most recent version

of the DSM there is a broadening of the many criteria, allowing for increase eligibility and

medication of people. (Frances, 2012). While this may result in an increase to medical care, we


must ask ourselves if we have created a culture of disorders. This is by no means suggesting that

we should not diagnose or medicate those with mental disorders; rather we should look at how

we are diagnosing and why. Exporting Euro-American diagnostics via ICD-10, spreads how we

conceive and diagnose what is considered a mental disorder in our culture, with the potential to

over diagnose around the world.

What now?

We would be correct to question the current diagnostic system and motives behind

creating those diagnoses. However, that does not mean we should disregard the criteria

completely. These disorders are prevalent across cultures, but should be divorced from the

pharmaceutical industries and the APA. One potential way to do this is to have non-task force

clinicians and mental health workers around the world more actively involved in creating the

diagnostic criteria, outside of the DSM task force panel, into an international database which

could log region specific symptoms, treatment plans and what works for various groups of

people. Giving communities access to these technologies could help democratize how we

diagnose and what is considered “disordered” in a culture. Additionally, this open diagnostic

system could help to de-stigmatize disorders by the very nature of looking at what is common

across cultures. This database would live online and could be added to by interested individuals.

The author does note that this does exclude communities without internet access. Once this

online database is functioning, efforts should be made to connect with the non-connected

community through by alternative types of data collection in different regions. To understand

how symptomatology and diagnosis connects to an alternative way to view mental health, we

must explore the roles of pervasive computing, big data and interfaces.


Brain-Body Perspective / Center Hub Open Source Software

Background

The term “Ubiquitous Computing” (UbiComp) has many names and variations. It is also

known as, “Everyware”, “Ecology of Things”, “Haptic Computing”, “Ambient Media”,

“Ambient Intelligence”, “Things That Think”, and the “Internet of Things” (IoT) - to name but a

few. Throughout this paper, UbiComp and IoT will be used interchangeably. UbiComp was

introduced by Mark Weiser in his seminal article titled The Computer for the 21st Century.

(Weiser, 1991) More recently the term Internet of Things (IoT) is the name of choice. All of these

connected autonomous devices are growing in number and popularity. According to a recent ABI

research report commissioned by Verizon, “… the IoT market is likely to experience strong

growth, rising to 5.4 billion connections across the globe by 2020, counting cellular, fixed line,

satellite, and short-range wireless connections, up from 1.2 billion devices today.” (Verizon IoT

Market Report, 2015) According to the European Research Cluster on the Internet of Things

(IERC), the Internet of Things is defined as:

“A dynamic global network infrastructure with self-configuring capabilities based on standard and

interoperable communication protocols where physical and virtual “things” have identities, physical

attributes, and virtual personalities and use intelligent interfaces, and are seamlessly integrated into the

information network, often communicate data associated with users and their environments.” (IERC

website, accessed April, 2015)

This UbiComp (IoT) world that Weiser envisioned had clear delineations between

human-machine relationships. According to Weiser,

“What Ubiquitous Computing Isn’t: Ubiquitous computing is roughly the opposite of virtual reality. Where

virtual reality puts people inside a computer-generated world, ubiquitous computing forces the computer to


live out here in the world with people. Virtual reality is primarily a horse power problem; ubiquitous

computing is a very difficult integration of human factors, computer science, engineering, and social

sciences.”(Weiser website, accessed April, 2015)

However, Phil van Allen in his conceptual framework writings, Models, re-imagined IoT

from an organic perspective called The New Ecology of Things. According to van Allen, “The

New Ecology of Things is a model that implies an organic, evolving system influenced by the

actions of its inhabitants (both people and things) and the circumstances of its

environment.” (van Allen, 2008) The reason this approach is relevant is that this neural

networked approach includes the environment as well as the strengths and limitations of the

physical items and personality of the people. While a smart system can learn preferences, and

types and times of behaviors, van Allen points out the personality / networked object approach,

that was missing from much of the IoT literature to date.

Current Direction

Currently, we see a melding of human and machine along with the real and the virtual

and these lines will only become increasingly blurred with new advances in technology. As these

connected “things” are becoming more intelligent and many devices, applications and home

systems are using a neural network based approach by learning through recognition.

Additionally, new research from Defense Advanced Research Projects Agency (DARPA)

suggests that we are moving in a direction which will allow us to chemically code to neurons and

project and manipulate sensors within the brain (Alvelda, 2015). The potential to transform a

virtual reality into a perceptual reality means that computers implanted in our heads and virtual

reality could co-exist and meld in the same person, in the same world. Our future world could be


an extension of and opposite to Weiser’s original ideas about Ubiquitous Computing. This IoT/

AI combination has the ability to expand our concepts about how to design.

One focus that is not highlighted in this IoT is the relationship of the individual with IoT

systems management. In their book Internet of Things: A Hands-On Approach, Arshdeep Bahga

and Vijay Madisetti explain how to manage IoT systems and assess the strengths and limitations

of this system. (Bahga & Madisetti, 2014) For example, they discuss specific Network Operator

Requirements (Bahga & Madisetti, 2014, pg. 82), Network Configuration Protocol (Bahga &

Madisetti, 2014, pg. 83) and the data modeling language YANG (Bahga & Madisetti, 2014, pg.

84). According to Bahga and Madisetti, the Internet Architecture Board via the Internet

Engineering Task Force (IETF), is troubleshooting how to address issues with network

management protocols.(Bahga & Madisetti, 2014, pg. 82) I propose that in conjunction with

these changing network configurations we develop an open source software, which will enable

the user to control how apps and data are managed within the network. This approach would

include an understanding of the current IoT network, but would also include the New Ecology of

Things perspective, which considers physical and personality configurations of these networks,

all encompassed in a brain-body perspective.

Understanding that the physical and graphic interface of one central hub could help to

address potential issues from having disparate systems, sensors and apps operating in different

hubs (home hub, car hub, etc). This could greatly affect the human experience when we have

hundreds of computers and sensors, operating in different networks and ecologies, we must

figure out how to understand ways to organize and regulate them in a way that the average

consumer can confidently control their digital profile and privacy settings. Circling back to the


brain-center perspective, imagine that your fingers were programmed to move one way through

one app in a particular environment – if the environmental conditions were unintentionally met,

your fingers could start behaving in a disjointed manner, inconsistent with another environment.

If these computers start to interact with our body, we need to organize our software in a way that

would include these hubs and their various apps, and mirror the human brain.

In a recent New York Times piece, Molly Wood describes the Internet of Things: “The

term refers to the concept of a world full of connected devices controlled through a consumer-

friendly hub, like a smartphone app.” (Wood, 2015) Other consumer friendly “hubs” for these

“things” including the developer platform Revolv, purchased by Nest in October, 2014 for the

“home hub” or the “car hub” which could exist in human driven or driverless cars.

In the literature to date, there are conflicting ideas about how to manage these things of

the internet. For example, Molly Wood suggests a smartphone app as a user friendly hub,

whereas Revolv/Nest view the home hub from a software platform perspective. If we take the

IoT and the personalization of IoT from the New Ecology of Things perspective, we can then

start to imagine how to design for these separate hubs via a central hub in a way that empowers

the individual to manage how they understand and change their data.

New Direction

Another way to look at this is by reframing UbiComp as a brain-body interaction and

processing these computer-sensors like our senses, through a hub (brain, software). The central

nervous system would be equivalent to wireless local area networks. This perspective would also

take into account independent and dependent computing, just as our brain can have independent

systems operating separately from the rest of the body. The inclusion of the brain-body


perspective and combined with the independent systems perspective is the important element that

is missing from UbiComp research to date.

Brain-Body Center of Systems, which is an open source software management system

would mimic the function of the brain within the body. One of the advantages of having this

“center” would be more control individuals have of how their “digital profile” is used across

environments. This “center” would essentially be a hub of all hubs, a central area where you

could connect your phone hub and your house hub and your car hub, and all of those separate

smart ecologies in one area. This “center” could change the functions of a device across hubs,

track your personal data, and tie in with a private version of your electronic medical records

(EMR). Some of this would happen through the existing programming of smart devices modified

to become a part of this “center”.

An open source development perspective for the creation of this central software

management would be great, as it would not favor one particularly system, and allow all of the

other pre-existing systems to connect with and build upon the software in this “center”. This

“center” would include a user friendly interface, while at the same time include privacy settings

that could be opted in and out of per app. So, say one is only able to use their pedometer app if

they accept the terms of use, which includes an undesirable privacy statement. In the interface

you could click off of these devices, removing your pedometer and that app within your center

hub and all other hubs. Then, others in the open source community could upload a non-privacy

invading version of this pedometer based on demand. One drawback of the addition, removal

approach of these sensors and applications is potential loss of the “smart” data and behavior

preferences collected from the privacy offending app. One caveat to those businesses that would


connect with this “center”, would be the requirement that the learned and cooperative behaviors

are still in this system, even if the device/app is removed. An example of this would be if your

pedometer is tied your exercise goals app on your computer. The other smart devices connected

with the goals app would still utilize previous behavior information.

How does this connect to the mental health perspective? Imagine what this world would

look like for someone with a major depressive episode with a specifier of Seasonal Affective

Disorder whose name is Sally:

Sally’s Morning

According to her physician, Sally needs one hour of indirect exposure to sunlight and 20

minutes of light therapy per day. On days when little sunlight is available to her, she is to

double her light therapy intake to 40 minutes. Sally wakes up in the morning to her blinds

slowly moving with the light therapy bulb emitting through the blinds. The carefully

calibrated light therapy sensor combined with the weight sensor under her bed, can read

the amount of light that she receives while sleeping in the morning. Once she leaves her

bed, the light is turned off and her morning light exposure is sent to the home and central

hubs. Once out of bed, she walks to her bathroom, brushes her teeth and takes her

morning shower. Then, she makes her way into her kitchen where she has a coffee, and

takes a juice from her refrigerator and a breakfast bar from her pantry before rushing off

to work. During Sally’s lunch break at work, she puts on her light therapy hat for an

additional five minutes. On her way home her car audio system buzzes and tells her that

she is approaching the gym and has met four of her five workout hours for the week.

Sally becomes annoyed that her car hub and central hub are micromanaging her behavior,


so she tells the central hub to deactivate exercise goals with a simple voice command.

That way she is not cued to engage in physical exercise in the rest of her environments

throughout the week. When later questioned why she deactivated the exercise goals, she

tells her therapist that she feels healthy, and would like to slowly start to remove these

devices from her life.

Sally’s Depression

A year ago, Sally was in the middle of a major depressive episode. After months

of lethargy, and a disinterest in what she had previously considered pleasurable activities,

Sally went to her local doctor’s office and referred out to a mental health professional.

Having recently moved to Alaska from sunny California, the change in sunlight had a

tremendous effect on her mental state, mood and energy levels. Sally and her therapist

worked together to subtly modify her environment to monitor the amount of light she

received per day, along with her diet and exercise habits. Sally has always been social

and felt that extended this digital monitoring into her social life would be invasive, but

she felt happy and comfortable with adding a few sensors to her home and reconfigure

her normal toothbrush sensor timer, to a toothbrush sensor use. Throughout her therapy,

Sally could see that her physical hygiene and food consumption increased with her light

therapy treatments. After a few months of treatment she was able to incorporate her old

exercise routine and reported feeling “back to her old self”.

What was at once incredibly helpful is now starting to feel invasive. Both Sally and her

therapist decide to keep the light therapy and light therapy sensors active, but to deactivate

everything else. Sally was happy to have used her environment to give her the extra support she


needed during a challenging time, but is equally as happy to continue her life without them. The

central hub allows her to make large decision changes across her environment.

The story of “Sally’s Morning” was exactly a year from her initial diagnosis with Major

Depressive Episode with the seasonal pattern specifier. She now happily lives in Alaska and

actually looks to her devices to give her clues to her emotional states that she is not always aware

of. Looking at this from another perspective, these computers contribute to the creation of a

digital affect display. According to the American Psychiatric Association, affect display is a

display of emotion “by facial, vocal, or gestural means” (APA, 2006, p. 26). The overall digital

affect of our hubs (home, car, phone) can give ourselves and mental health care providers a clue

as to our mental health states. For example, the devices in her home helped to measure her

“home digital affect”, including things like how often she brushes her teeth, and how often she

showers and opens the refrigerator door are digital clues of behavior patterns.

This technology discussed in the story of Sally is not new – we can easily create the

home hub scenario above, but what is new, is the way we can use this information for those who

suffer from mental health related distress or suffering. As many of the pervasive computing

authors have suggested, privacy is a huge concern with Ubiquitous Computing. Even more

concerning is combining invasive data collection in order to minimize or address mental health

disturbances. What does it mean for companies to have our “home digital affect” or our “phone

digital affect”? The ability to eventually reveal our mental states via our devices, and shopping

behaviors is a huge concern and will be further explored in the next section of this paper.

!!


Data + Privacy

In a recent 60 Minutes episode titled, The Data Brokers: Selling your personal

information, Steve Kroft said, “We are giving up more and more private information online

without knowing that it's being harvested and personalized and sold to lots of different

people.” (Kroft, 2014) This realization that we are giving a lot of our private data in an

unregulated environment conflicts with our desire for privacy. This could lead to a skewing of

marketing of our taste preferences and our actual preferences in a digital world. The concern is

not only overt breaches of privacy, but subtle changes in our preferences through algorithms

combined with advertisements – at some point it is hard to differentiate between the two. An

example of this is customization in many music subscription services. Sometimes I don’t know

what I requested to play or what Pandora is suggesting; hence my concept of my preferences is

no longer rooted in my actual preferences.

Another example of data analysis and advertising is where we rely on devices to tell us

when something is wrong. Could data reversal program our devices to think we have an issue? If

Sally’s refrigerator started behaving strangely, by indicating that Sally needed a certain type of

grocery that she never consumed before, would she be alerted to a potential health problem, or

would the reality be that a marketer had paid for her third party information to target ads to her

refrigerator? In the future will you have to pay to not receive target ads or behavior changes to

your environmental computers? We currently have the food ordering technology in smart fridges,

however what will happen when ads created from our purchase history are co-mingled with our

grocery lists.


The reality is, we don’t know what happens or will happen to the majority of our data,

just as many of those collecting data don’t know the actual use at the collection level and at the

research level. Helen Nissenbaum in her book Privacy, Big Data, and the Public Good, writes

about this issue, “With the best of intentions, holders of large datasets willing to submit them to

analyses unguided by explicit hypotheses may discover correlations that they had not sought in

advance or anticipated.” (Nissenbaum, 2014, pg. 60) Nissenbaum provides two examples

behaviors alerting others to physical and mental states: the New York Times article about Target

predicting pregnancies before even the women themselves knew they were pregnant (Duhigg,

2012) and outrage over a paper which published findings based on student internet behaviors and

correlation with depression. A more recent concern is Samsung’s Smart TV. Electronic Privacy

Information Center EPIC, filed a complaint with the Federal Election Commission (FTC)

regarding concerns over the Samsung’s privacy policy which states “…please be aware that if

your spoken words include personal or other sensitive information, that information will be

among the data captured and transmitted to a third party through your use of Voice Recognition.”

(Riberio, 2015) EPIC is a public research group that focuses on emerging privacy and civil

liberties issues. In their lawsuit they call out the harm that Samsung’s privacy policy does to

American consumers. (EPIC complaint against Samsung, FTC, 2015)

Business are taking advantage of the potential benefits of large scale data collection,

marketing and IoT." We see the Internet of Things as an extension of our machine-to-machine

business, as does everyone else in the marketplace today," says Mark Bartolomeo, vice president

of IoT Connected Solutions at Verizon. "When you think about machine-to-machine, what you're

really looking at is connecting devices and collecting data from those devices. The next big leap

http://www.techhive.com/article/2881653/shhh-your-smart-tv-is-eavesdropping-on-you.html


for IoT is really about the use case. IoT is about interconnecting these devices for specific use

cases and deriving value from the data.”(Olavsrud, 2015)

On the other hand, Data and Ubicomp could be helpful in warning us about upcoming

triggers for specific disorders, like a child on the Autism spectrum visiting a park. A phone app

or smartwatch could vibrate to indicate a stimulus rich area is approaching, similarly, your

smartwatch might buzz before a rock show to warn epileptics that strobe lights will be used. Or

perhaps in the future a combination of these wearable devices might help to alleviate symptoms

experienced by those suffering from mental disorders. An example of this is temperature

regulation difficulties due to disorders themselves or more likely, the side effects of the

psychotropic medications, mitigated through temperature regulated clothing. (Labarre, 2010)

These are examples of individual sensors communicating in real time with a community.

However, what happens when markets tracking your app or sensor purchases target your specific

disorders? While this happens in our current world, what would that look like in a more

physicalized form – if our devices react to certain inputs how can we control how this data might

interact with other clinics? For example, what if an alcoholic’s watch changed color every time

she/he walked by a designated AA location? What will happen when these systems become so

intelligent that they don’t even know what they will do and data starts to behave on its own and

make decisions regarding what it thinks you should be alerted to or will enjoy? This is why the

concept of the central hub is important, because with this central control area, we must write into

our software controls to de-activate unwanted devices and data collection behaviors.

Another concern regarding data is the increasing intelligence of the devices used to

gather this data. If we move from consider that “things” will be moving from strong AI to weak

http://www.cio.com/author/Thor-Olavsrud/


AI with an increased capacity to learn and problem solve, we can teach our hub profile what to

share and what not to share, almost like a digital bodyguard. According to many scientists, it is

unlikely that strong AI will happen on our lifetime (Amyx, 2014). As these systems are not smart

enough yet, the concern is they could be used in conjunction with insurance offices and

government regulators. We must be very careful not to give wearables, IoT and businesses too

much power over these (for the time being), dumb devices.

Concerns

This prevalence of connected devices are projected to grow substantially, but with that

growth comes increasing concerns over privacy. Adam Greenfield discusses the wonders of

“Everyware” (IoT) but he is careful to warn about the potential for our invasion of privacy and

its effects on our lives. Greenfield writes that computing has “leapt off the desktop and

insinuated itself into everyday life.” He coins the idea of “everyware,” which he further

describes as ubiquitous information technology (Greenfield, 2006, pg. 9). In a talk at Keiko

University in 2006, Greenfield warns about the rise of devices and an overarching concern in

regard to our privacy. Greenfield compares Michel Foucault’s internalizing models of control

and the panopticon with everyday life and IoT. He gives the example of an internet-enabled toilet

that analyzes human waste with the capability to transmit its findings to a variety of recipients

such as doctors, insurance companies, parents, and law enforcement agencies. He claims this

refigures what Foucault meant by “the gaze”, and claims that this type of biometric monitoring is

“the new gaze”. He states, “once we allow systems of this nature into our environment we will

have a very, very hard time getting rid of them, so we had better be careful to that we design


them to be conscious of our values and to represent a world that we want to live in.” (Greenfield,

2006, 25:00)

Greenfield also explores our potential to change our concepts about privacy through the

examples of personal computing and everyware. Greenfield provides an example of his friend

using a personal computer choosing to engage, in a conscious action over which he has entire

agency. He describes this as a one to one relationship with the computer and no other devices in

his environment; he says the personal computer has to be everything to his friend. Greenfield

argues that in this situation, his friend has a high degree of presentation of self. He believes that

this is important in personal computing because you are interacting with the world in a specific

way that is chosen or managed. Greenfield provides a counter to this in his concept of everyware

(IoT). He says that everyware does not allow for specific, personal management of your

interactions. To illustrate this, Greenfield gives another example of two friends eating and

suggests that in the future, things like cutlery will have RFID chips and water carafes will notify

the wait staff when they need to be filled. Greenfield's concept of everyware is that we will not

manage these interactions, yet they will become embedded into our life, and “dissolve into

behavior”. Greenfield suggests that one byproduct of this possibility is that this automation will

reframe how we consider privacy. He states that while the body is irrelevant in personal

computing, in everyware the placement of the body does matter (due to sensors). This again ties

into that idea of a “new gaze”, and a world where our bodies essentially become a device for

multiple sensors. This means that, unlike the personal computer, we lose control over our

presentation of self.


Dourish, Bell, Rogers and Greenfield all mention privacy as a key issue concerning the

future of IoT. Dourish and Bell look at privacy from a cultural ordination perspective and

consider the sharing of information and negotiating meaning collectively. Rogers writes that in

IoT “…much of the discussion about the human aspects in the field has been primarily about the

trade-offs between security and privacy, convenience and privacy, and informedness and

privacy.” (Rogers, 2006, pg. 410) She gives a specific example of the invasion of privacy

experienced by older adults monitored in their beds in a nursing home and generalizes this to

privacy in general. Like Greenfield, she refers to potential invasion of privacy sensors as

“panopticon developments”. Lastly, Greenfield’s concept of the “new gaze” is present and

operating right now in the United States, as evidenced by the emerging popularity of wearables,

access to our private data, and regulations concerning access to our private health data.

Another major concern regarding privacy is our physical and mental health data here in

the United States. In a recent lecture, Art Center Professor Phil Van Allen spoke about the

benefits and potential disadvantages of digital data storage, specifically concerning medical

records. He suggested one possible way to address these concerns is to have your medical

records independently stored with a not for-profit business. (Van Allen, 2014) Nodes of your

EHR could connect to your “central hub”, however the user should decide which nodes she/he

would like to incorporate.

Access to medical information is a timely and significant concern. An important

regulation found on the United States Department of Health and Human Services website, about

The Patient Protection and Affordable Care Acts requires “standardize billing and requires health

plans to begin adopting and implementing rules for the secure, confidential, electronic exchange


of health information.” (HHS website, accessed 2015) This movement to electronic storage of

our medical records is highly concerning from a privacy perspective. More specifically, the

government having access to citizens’ health records is concerning. Most concerning, is the

potential ramifications of these regulations on mental health records.

A present day example is how psychologists and psychiatrist record their therapy notes

and patient information. Psychologists submitting insurance claims must maintain electronic files

as required in the Affordable Care Act. However, at the same time they have an ethical and legal

(Health Insurance Portability and Accountability Act) requirement to maintain confidentiality. It

is one thing to have your health records on a central database, but quite another to have your

mental health records in a networked, digital health database (as is the case with electronic health

and electronic medical records). This concern has been highlighted in multiple situations when

patients’ mental health notes have been included in networked records system.

A 2012 Boston Globe article by Liz Kowalczyk highlights that very situation. In 2012,

the Partners HealthCare system made psychiatric notes of a patient named Julie available to their

entire networked system. When Julie stated that her notes were “nobody’s business”, Sprawling

said, “Doctors must have a complete picture to make accurate diagnoses, the organization argues.

And having different rules for psychiatric records contributes to the stigma of mental illness.”

This is highly disturbing because access to electronic medical records will be government and

statewide, as well as in-network to healthcare providers. Now imagine a world in which

ubiquitous computing and the emerging field of brain machine interface provide new

opportunities for sharing of patient data. When we have the ability to track via sensors, and


through implants, where do we draw the line between privacy, data collection and government

regulations?

Our “phone hubs”, our “home hubs” and our “car hubs” are continually gathering

information, and chances are, when that weight loss app on your phone was downloaded, you

accepted terms of use statement that you did not read. There is a high likelihood that for many of

your apps, you have no idea how your data is shared. Having a central hub to identify what third

parties our data is sent to, along with visualizing all of your disparate hubs will add some

understand to our connects selves and our environment.

Mental Health Perspective

The interesting thing about the Monitoring Internet Patterns is that Montgomery,

Chellapan, Kotikalapudi, Wunsch and Lutzen’s find “…monitoring online behavior revealed

patterns associated with symptoms of a mental health problem is import for social science

research and its focus on understand human behavior and behavioral problems.” (Chellapan et

al., 2013, pg. 5) Their findings revealed that how the research subjects used the internet

correlated with an increased rating on Center for Epidemiologic Studies Depression Scale.

(Radloff, 1977) Using internet usage to predict or identify mental disorders could have real

clinical and treatment value, however the very analysis of this data is invasive on a different kind

of level, in that it points to a mental health disorder when a person is potentially unaware. The

thought of a corporation knowing my mental state before I do is scary. Now combine this

concern with government oversight – it makes me wonder if at one point we will be required to

regulate our mental health if we know we have a disorder. If our concepts of “disorder” is


expanded by big pharma, then could we not get in a loop of continued diagnosis and treatment

with no eye to the culture that is creating this loop.

We don’t know what our data will be used for in the future and since we have an increase

in IoT devices we should be concerned. A more connected individual also means a more

monitored individual. Additionally, if our data is fluid, even if we have a central hub which

would allow us to remove devices and apps and programs that we feel could violate our privacy,

it would be difficult to try and convince these app and device makers to code their devices to be

easily removed from this hub. It would make very little economic sense for them to bypass

selling data to third parties.

Coding

There are multiple types of coding throughout this mental health approach. The coding of

the mental health disorders, the open source coding and more recently, the neural coding. These

elements were grouped together to highlight the difficulties and nuances of these multiple

systems and how they interact.

Mental Health Diagnostic Coding

There are various systems on both the mental and physical health side that require

compliance with insurance. Whatever EHR management system is used, the medical coder or

individuals who diagnose must make decisions based on the information they have, to offer the

best coverage for their clients. For example, when diagnosing autism clinicians use

developmental screening and a comprehensive diagnostic evaluation. Additionally, the diagnostic

team would use both the Autism Diagnostic Interview - Revised (ADI-R) and the Autism

Diagnostic Observation Schedule (ADOS) to confirm a diagnoses. The tricky thing with


Diagnosing and scoring is when a child with autism is very clearly autistic but doesn’t quite meet

all of the diagnostic criteria. Technically and (some argue) morally, that clinician(s) should not

give a diagnosis of Autism Spectrum Disorder unless the exactly fulfill the diagnostic criteria.

However, the reality is, without a specific diagnosis, the child’s access to care is severely limited

and then the parents are responsible for hundreds of thousands of dollars throughout the lifetime

of their child. Additionally, many individuals on the coding side, be it mental or physical health,

have the best interest at the client at heart and are trying to get them the treatment they need. At

times, what results is slightly inaccurate diagnoses or over diagnoses. However for those with

severe disorders, the actual diagnoses is not necessarily the issue, rather the ability to receive

services and treatment that is felt by the client.

From this perspective, the big pharma loosening of certain diagnostic criteria discussed in

the DSM-5, does not appear to be such a bad thing. Similarly, in the medical community it is

often up to the discretion of the medical coder, how they code. There must be familiarity with the

diagnosis, and additionally the coder must comply with the unspoken requirements of the

hospital to sway potentially gray areas. The possibilities for false and / or exaggerated diagnosis

is within our pharma and insurance system and must be continually navigated by clinicians.

Open Source Software Coding

The developing of open source software (open development), is a process by which

coders have access to the source code, which can be modified. Individuals can develop programs

aimed at working with the software. As suggested earlier, if the central hub is an open source

software management system, then coders, designers and innovators can create non-privacy

invasive apps and plug-ins. The main advantage to an open source central hub software


management system is that no single business or government would control it. Additionally, free

open source software would encourage people from other companies to modify the existing

software and connect their “hub” to this open source software. For example, you could connect

your home nest revolv system to the central hub so it could work with your nest and connect that

data to your car.

Bagha and Madisetti discuss the ideal configuration of systems management. This is

important because they are talking about systems management. The central hub proposed is a

software operating on a NETCONF protocol YANG (data modeling language) (Bagha and

Madisetti, 2014, pg. 4). This open source software would have to work with pre-existing systems

management.

Neural Coding

During her 2011 TED Talk, Shelia Nirenberg codes signals to retina output cells. She

states that photoreceptor cells receive images and then the retinal circuitry works to “preform

operations on it and then converts that information into a code, which is a pattern of electrical

pulses which is sent to the brain.” (Nirenberg, 2011) She says, “When I say code, I actually mean

code. This pattern of pulses sent to the brain actually means baby’s face. When the brain gets

those patterns of pulses it knows that what was out there was a baby’s face. She goes on to say

that the patterns and pulses change all of the time. She built a device that mimics the front end

retinal circuitry and send signals to the retinas output cells. Her eye prosthetic attempts to do this

via the encoder (input) and then the transducer which sends out “normal” signals to the brain.

She says this happens by putting a set of equations on a chips – she abstracted what the retina is

doing and then is coding it via electrical pulses to the brain. (Nirenberg, 2011) DARPA is


building upon this, and working on the creation of a “cortical modem”, which would place

images on the actual visual cortex. (Alvelda, 2015)

From a mental health perspective, what might this mean to design retinal code signals for

the mentally ill? Are there calming universal images that we could use? Perhaps we could code a

pictorial memory to evoke in emotionally turbulent times. What would it be like for someone

with normal vision to have a code of a picture sent to their brain? I would imagine the result

could have the potential to be quite disturbing. However, if we are able to code to other areas of

the brain, say an audio or olfactory equivalent to make the memory more salient, would this

virtual reality be believable to the body? Could we code orderly images and white noise for

children suffering from autism?

Interface

IoT + AI + BCI

Not only are we breaking down the codes of the human body, we are also implanting

computers within our bodies to interact with external devices and for specific mental health

treatments. No longer is the world a simple demarcation of technologies. UbiComp, AI and BCI

are becoming increasingly interconnected. A recent Wired article suggests that wearables and

other IoT implementations work primarily with weak AI, which focuses on very specific tasks or

reasoning. (Amyx, 2014) However, a gradual move to strong AI will reframe how we connect

with our environments. When the individual “things” in the Internet of Things all incorporate

some level of artificial intelligence through things like neural networked chips and when we

slowly start to incorporate more elements of strong AI, then our “things” could actually have

multiple levels of utility, as an individual device and as a networked device.


We also need to think about how to program differently. In a DARPA.tv YouTube video,

Just Sanchez talks about how programmers need to try and create a symbiotic relationship

between machines and humans. (Sanchez, 2015) He gives examples of how the current Nest

technology can learn our behaviors by looking at the temporal sequence for optimal room

temperature and can program the temperature based on previous inputs. However, where the

Nest fails is in understanding individual changes in physiologies. He gave the example of

someone exercising at home, and how the Nest does not account for the individuals change in

temperature. He points out that “…we are too focused on the aggregate of this technology, rather

than the signals that give rise to that (state change). Machines have no idea about the functional

relationship between the two.” (Sanchez, 2015, 11:57) This concept is important when

considering context relevant state changes in mental health. When considering these nuances,

this concept of signaling technology rather than just aggregating technology is important when

we consider interacting with tools that measure emotions, such sentiment analysis includes voice

analysis, face recognition, and language usage.

While the Internet of Things includes interacting with tablets, phones and wearables,

more interestingly, it is also about brain-computer interfaces. As previously mentioned, we are

starting to understand certain codes of the brain, and how we can directly code to the body. I

propose that this will trickle into our everyday devices and I think by considering this future

when we design for the brain, we should consider that our devices will be networked across

various senses. Sally’s blinds could connect directly into the retinal code, as well as the auditory

code. In this software system and in the real world, we should design for such things, so we can

understand what potential external devices could do to us. These direct coding plugins, accepted


in an unfamiliar environment, could result in atrocious actions to an innocent and wired brain. I

propose that we put visual markers on Sally’s blinds something abstract and concrete – perhaps a

little image of an eye and an ear, so the user knows that this is a smart, coded device that could

interact with her brain. Sally’s interaction with the central hub could allow her to respond or not

respond to the blinds. More interesting, what would it look like when these blinds are

accountable to two individuals in one home? What if each participant has a separate emotional,

physical and mental health profile that conflicts with the other profile on the device? I wonder

what this confusion would look like in a community setting. Do objects then start to become

accountable only to certain people? Does Jane Doe then own the blinds of a dorm hallway? How

do we design for such potentially conflicting experiences?

With the newness of BCI we should now consider how to design not only for the

external, but internal states as well. Is the new role of designer to internal emotional experiences

through the brain?

Conclusion

From a mental health perspective, one of my first goals with wearable technologies was

to create a symptom tracker for mood disorders. Taking pre-existing and modified tracking

information, a user could easily swipe to indicate their mood and answer a few questions on a

smart watch via voice commands and questions. The reason that this is important, is that your

remove an extra steps currently involved when tracking and logging symptoms on a smart phone

app. For recording emotions, the process must be as seamless as possible. From a user

experience perspective and a mental health perspective, you cannot ask people to give too much

thought to their emotions, otherwise overthinking or disregard may occur. Mental health


wearables are on the way, and more importantly the IoT will soon have a mental health focus that

we are currently seeing with health tracking and sensing wearables. It is important to think about

how we are to manage such technologies, especially when we eventually connect modules of our

electronic health recode with the IoT hubs.

In a recent lecture, Art Center Professor Phil Van Allen spoke about the benefits and

potential disadvantages of digital data storage, specifically concerning medical records. He

suggested one possible way to address these concerns is to have your medical records

independently stored with a for-profit business. (Van Allen, 2014) Adding Nissenbaum’s concept

that data’s intended use often changes, we can see data as not so protected and fluid. One core

feature to having an open source software / central hub, is that as individuals, coders and other

community advocates, we can work to take back control of our data via the software itself.

Additionally, we might not want to try and control everything, as long as we are comfortable

with how our digital profile is engaging with the world, and how it makes us feel as a person.

We must also consider what we are doing with these technologies. In the first part of my

paper, I discussed the potential of exporting American concepts of mental illness via the ICD-10.

If we were able to utilize a symptom tracker in different countries, at some level, aren’t we

colonizing the private and mental health space of those individuals with invasive diagnostic

symptoms? While development of a symptom tracker is certainly the direction we are moving in,

we must be mindful of how and why this technology will be used. Is it to truly help those

suffering from mental illnesses? It this technology helping to remove stigmas and create

awareness about this suffering? Or are we essentially working for big business and pharma under

the guise of in “helping” and “data collection”.


In this paper I explored diagnosing, data collection, coding, and interfaces and looked at

how those elements will shape the way we design for mental health in the future. I explored the

positives and the negatives associated with these perspectives and provided scenarios and

examples of what this could look like in the present, near present and future. I also looked at

what it means to design for neurons, to create positive mental heath experiences through

chaining the body’s concepts of reality. This too, could have negative ramifications. I ask the

question, could we eventually get to a place where we are no longer accountable for the full

functionality of our bodies? Could we remap faulty cognitive maps that contribute to human

suffering via remaking our senses, or could we hack into someone else's brain and commit a

crime? These are questions we should continue to ask as these changes in technology arise. A

benefit from using technology to track cognitive distortions is that technology can feel more

neutral and less emotional when it comes from a machine or computer, rather than a human. This

could be ideal in designing programs for individuals with personality disorders.

Finally, we must recognize that this system of design is from a Western perspective and

this concept of mental health is also from the West. While you cannot ignore that the central hub

software was conceived from a Western mind, the fact that it is open source means other cultures

can remix as they wish. The idea is to demystifying and connect much of these disparate items so

people can understand and modify their experiences as they see fit. The closer we get to

mirroring the design of the brain, I argue the less gender specific or race specific we become. For

example, if each individual sets up their hub to mirror their individual bodies and senses and

environmental interactions, it is a step towards focusing on broader human physiology rather

than the white male in the United States.


Moral considerations must also be explored. Rogers writes that in UbiComp, “…much of

the discussion about the human aspects in the field has been primarily about the trade-offs

between security and privacy, convenience and privacy, and informedness and privacy.” (Rogers,

2006, pg. 410) She gives a specific example of the invasion of privacy experienced by older

adults monitored in their beds in a nursing home and generalizes this to sensor information and

privacy in general. Like Greenfield, she refers to potential invasion of privacy sensors as

“panopticon developments”. What does a sensing and monitoring environment mean for children

and their development and sense of privacy? How will we design in ways to protect that in these

new systems?

The design of the future will be focused on designing for the brain and how IoT networks

outside of the brain will interact with neural networks within the brain. This then makes us

question, what is the role of the designer in this space? Is it to try and minimize suffering? Create

beauty? Or perhaps a little bit of both.

!!!!!!!!!


References

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders

(5th ed.). Washington, DC: Author.

American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders

(4th ed., text rev.). Washington, DC: Author.

Amyx, S. (2014, December 1). Wearing Your Intelligence: How to Apply Artificial Intelligence

in Wearables and IoT. Wired.

Alvelda, P. [DARPAtv]. (2015, March 10). Phillip Alvelda - Program Manager: Beyond

Prosthetics [Video file]. Retrieved from https://www.youtube.com/watch?

v=z3AuOVyHFCE

Bahga, A., & Madisetti, V. (2014). Internet Of Things: A Hands-On Approach. Arshdeep Bahga

& Vijay Madisetti.

Collier, R. (2010). DSM revision surrounded by controversy. CMAJ  : Canadian Medical

Association Journal, 182(1), 16–17. doi:10.1503/cmaj.109-3108

Cosgrove L, Krimsky S, Vijayaraghavan M, & Schneider L. (2006). Financial ties between

DSM-IV panel members and the pharmaceutical industry. Psychotherapy and

Psychosomatics, 75(3):154-60.

Cummings, C. (2013, June 27). DSM-5 on Culture: A Significant Advance. Retrieved April 4,

2015, from https://thefprorg.wordpress.com/2013/06/27/dsm-5-on-culture-a-significant-

advance/

DSM. (n.d.). Retrieved April 4, 2015, from http://www.psychiatry.org/practice/dsm

!

https://www.youtube.com/watch?

https://thefprorg.wordpress.com/2013/06/27/dsm-5-on-culture-a-significant-

http://www.psychiatry.org/practice/dsm


!Dourish, P., & Bell, G. (2011). Divining a Digital Future: Mess and Mythology in Ubiquitous

Computing. Cambridge, Mass., MA: MIT Press.

Duhigg, C. (2012, February 16). How Companies Learn Your Secrets. The New York Times.

Retrieved April 3, 2015, from http://www.nytimes.com/2012/02/19/magazine/shopping-

habits.html?pagewanted=9&_r=3&hp

Frances, A. (2012, December 2). DSM 5 Is Guide Not Bible—Ignore Its Ten Worst Changes:

APA approval of DSM-5 is a sad day for psychiatry. Psychology Today.

Greenfield, A. (2006). Everyware: The dawning age of ubiquitous computing. Berkeley, CA:

New Riders.

Greenfield, A. [Internet Archive]. (2006, July 15). Everyware: The dawning age of ubiquitous

computing [Video file]. Retrieved from https://archive.org/details/

Hernandez, P. (2015, March 18). Health Wearables a $41 Billion Market by 2020: IndustryARC.

Retrieved April 4, 2015, from http://www.datamation.com/applications/health-wearables-

a-41-billion-market-by-2020-industryarc.html

Internet of Things. (n.d.). Retrieved April 4, 2015, from http://www.internet-of-things-

research.eu/about_iot.htm

Jessup, Leonard M., and Daniel Robey. (2002) The Relevance of Social Issues in Ubiquitous

Computing Environments. Communications of the ACM. !Key Features of the Affordable Care Act By Year. (n.d.). Retrieved April 3, 2015, from http://

www.hhs.gov/healthcare/facts/timeline/timeline-text.html

http://www.nytimes.com/2012/02/19/magazine/shopping-

https://archive.org/details/

http://www.internet-of-things-

http://research.eu/about_iot.htm

http://www.hhs.gov/healthcare/facts/timeline/timeline-text.html


Kowalczyk, L. (2012, June 21). As Records Go Online, Clash Over Mental Care Privacy. The

Boston Globe.

Kroft, Steve. (2014, March 9). 60 Minutes [Television broadcast]. New York, NY: Central

Broadcasting Service.

Labarre, S. (2010, December 10). Nanotech Clothing Warms in Winter, Cools in Summer.

Retrieved April 4, 2015, from http://www.fastcodesign.com/1662859/nanotech-clothing-

warms-in-winter-cools-in-summer

Lane, J., Stodden, V., Bender, S., & Nissenbaum, H. (2014). Privacy, big data, and the public

good: Frameworks for engagement. New York, New York: Cambridge University Press.

Montgomery, F., Chellappan, S., Kotikalapudi, R., Wunsch, D., & Lutzen, K. (2013). Monitoring

Student Internet Patterns: Big Brother or Promoting Mental Health? Journal of

Technology in Human Services, 31(1), 61-70.

Nasser, L. (2012, January 8). Do some cultures have their own ways of going mad? As

psychiatry revises its manual of disorders, it faces a sticky question: What to do about

“culture-bound syndromes”. The Boston Globe. Retrieved April 3, 2015, from http://

www.bostonglobe.com/ideas/2012/01/08/some-cultures-have-their-own-ways-going-

mad/fSuwCngcHbTpMZdBzSFagI/story.html?s_campaign=sm_tw

NIH Fact Sheets - Mood Disorders. (n.d.). Retrieved April 4, 2015, from http://report.nih.gov/

nihfactsheets/ViewFactSheet.aspx?csid=48

Nirenberg S. [ted.com]. (2011, October). A Prosthetic Eye to Treat Blindness [Video file].

Retrieved from http://www.ted.com/talks/sheila_nirenberg_a_prosthetic_eye_to_treat_

blindness?language=en

http://www.fastcodesign.com/1662859/nanotech-clothing-

http://www.bostonglobe.com/ideas/2012/01/08/some-cultures-have-their-own-ways-going-

http://report.nih.gov/


Olavsrud, T. (2015, March 20). Internet of Things connections to quadruple by 2020. CIO

Magazine.

Pavlok- A Personal Coach Wearable That Shocks You! (n.d.). Retrieved April 4, 2015, from

https://www.indiegogo.com/projects/pavlok-a-personal-coach-wearable-that-shocks-

you—2

Poland, J., Von Eckardt, B., & Spaulding W. (1994). Problems with the DSM approach to

classifying psychopathology. In G. Graham & G. L. Stephens (Eds.), Philosophical

psychopathology (pp. 235–280). Cambridge, MA: MIT Press.

Radloff, L. (1977). The CES-D Scale: A Self-Report Depression Scale for Research in the

General Population. Applied Psychological Measurement, 1, 385-401.

Ribeiro, J. (2015, February 26). Samsung's Smart TV 'surveillance' prompts FTC complaint. PC

World.

Rogers, Yvonne. (2006) Moving on from Weiser’s Vision of Calm Computing: Engaging

UbiComp Experiences. Ubiquitous Computing: 8th International Conference 404-421.

Sanchez, J. [DARPAtv]. (2015, March 10). Justin Sanchez - Program Manager: Brain-Machine

Symbiosis [Video file]. Retrieved from https://www.youtube.com/watch?

v=oiLKkWqMbz0

State of the Market: The Internet of Things. (2015, January 1). Retrieved April 4, 2015, from

http://www.verizonenterprise.com

Sterbenz, C. (2013, November 8). The Way We Diagnose Mental Illness Might Be A 'Mistake'

Read more: http://www.businessinsider.com/problems-with-the-

dsm-2013-10#ixzz3WJXg9fnb. Business Insider.

https://www.indiegogo.com/projects/pavlok-a-personal-coach-wearable-that-shocks-

https://www.youtube.com/watch?

http://www.verizonenterprise.com

http://www.businessinsider.com/problems-with-the-


Tartakovsky, M. (2011). How the DSM Developed: What You Might Not Know. Psych Central.

Retrieved on April 1, 2015, from http://psychcentral.com/blog/archives/2011/07/02/how-

the-dsm-developed-what-you-might-not-know/

van Allen, P. (n.d.). The New Ecology of Things - Media Design Program - Art Center College of

Design. Retrieved April 4, 2015, from http://newecologyofthings.net

van Allen, P. (2014, November 21) In class discussion and presentation. Art Center College of

Design. Wind Tunnel, Pasadena, CA.

VandenBos, G. R. (2006). APA dictionary of psychology. Washington, DC: American

Psychological Association.

Wearables Market: 2015 is Year That Will Make or Break the Smartwatch. (2015, February 17).

Retrieved April 4, 2015, from http://www.ccsinsight.com/press/company-news/2137-

wearables-market-2015-is-year-that-will-make-or-break-the-smartwatch

Weiser, M. (1991). The Computer for the 21st Century. Scientific American, 265(3), 94-104.

Wood, M. (2015, January 4). At the International CES, the Internet of Things Hits Home. The

New York Times. Retrieved April 3, 2015, from http://www.nytimes.com/2015/01/05/

technology/international-ces-the-internet-of-things-hits-homes.html

!!!!!!

http://psychcentral.com/blog/archives/2011/07/02/how-

http://newecologyofthings.net

http://www.ccsinsight.com/press/company-news/2137-

http://www.nytimes.com/2015/01/05/


Appendix A Christine Meinders. (2015). Open Source Central Hub Software Proposal !!!!

!!!!!! !

!!!!!!

phone hub car hub home hub

central brain hub (open source software with privacy controls

+ user friendly gui)


Appendix B Christine Meinders. (2015). Ideas Fair Proposal !!!!!!!!!!!!!!!!!!!!

Mental Balance and Brain-Com

puter Interfaces: D

esigning for Neurons

Christine Meinders (christine.m

einders@gm

ail.com), M

edia Design Practices, Art Center College of D

esign, 950 S Raymond Ave, Pasadena, CA 91105

BACKG

ROU

ND

Developm

ent Projects (MED

IA): B

oth

Yve

-Ala

in

Bo

is a

nd

Ch

arle

s S

an

de

r Pie

rce

dis

cu

ss in

de

xin

g.

“A

cco

rdin

g to

Pe

irce

, an

ind

ex is

“a re

pre

se

nta

tion

,

wh

ich

refe

rs to

its o

bje

ct n

ot s

o m

uch

be

cau

se

of

an

y s

imila

rity o

r an

alo

gy w

ith it, (…

) as b

ecau

se

it

is in

dyn

am

ical c

on

ne

ctio

n b

oth

with

the

ind

ivid

ual

ob

ject, a

nd

with

the

se

nse

s o

r me

mo

ry o

f the

pe

rso

n

for w

ho

m it s

erv

es a

s a

sig

n”. (h

ttp:/

/arth

ist.n

et/

arc

hiv

e/1

12

0)

Creative Technology (EMO

TION

IN M

OTIO

N):

Exp

lore

s s

ub

tle b

eh

avio

ral re

sp

on

se

s to

pro

gra

mm

ed

cu

es.

PROPO

SAL

De

sig

nin

g fo

r Ne

uro

ns is

a s

erie

s w

hic

h e

xp

lore

s

data

base

s, s

yste

ms, b

ig d

ata

an

d d

esig

nin

g fo

r

bra

in c

om

pu

ter in

terfa

ce

s fro

m a

me

nta

l we

llne

ss

pe

rsp

ectiv

e. F

or th

e e

nd

of te

rm a

ssig

nm

en

t, this

wÀÃÌ�«À��iVÌ��Ì�i�ÃiÀ�iÃ�Ü��iÝ«��Ài�Ü

�>Ì��Ì��Ã��i�Ì��Ài>`�>�`�Ü

À�Ìi�Ì��ÕÀ��iÕÀ��Ã�Û�>�iÛiÀÞ`>Þ�o

bje

cts

. Ad

ditio

nally

it will in

co

rpo

rate

the

ide

a o

f

usin

g th

e a

ffect o

f a d

evic

e a

s s

up

ple

me

nta

ry d

igita

l

dia

gn

ostic

info

rmatio

n. T

o s

ho

w th

is, I w

ill use

bab

y

L�Õi�>LÃÌÀ>VÌ��ÌiÀ��V��}�«�iViÃ�Ü��V��Ü

��V��«�o

n e

ach

po

ten

tially

bra

in c

on

ne

cte

d o

bje

ct. T

his

Ü��i�V�ÕÀ>}i�Ì��}�>L�ÕÌ�Ü

�iÀi�Üi�Ü

��`�ÀiVÌ�o

ur e

nviro

nm

en

tal in

form

atio

n a

nd

ho

w it w

ill be

ÕÃi`��ÕÀ�LÀ>��1®°�/��Ã��ì>�Ü

��Li�Ã�iÌV�i`�b

y c

reatin

g a

sh

ort v

ide

o, th

at w

ill sh

ow

ob

jects

Ài>VÌ��}�Ì��Ì�i��`�Û�`Õ>��Ì�i��i]�V>À]��vwVi�>�`�

oth

er e

xte

rnal e

nviro

nm

en

ts. A

n e

xam

ple

of th

is

wo

uld

be

blin

ds fo

r a M

ajo

r De

pre

ssiv

e E

pis

od

e w

ith

>�Ãi>Ã��>��«>ÌÌiÀ��Ã«iV�wiÀ°��

Figure 1 Co

rtical M

od

em

(DA

RPA

). Aug

mente

d R

eality

pro

jectin

g;

/�i�V�ÀÌ�V>��ì�

��Ã�À��Ìi`��Ì�i�wi�`��v��«Ì�}i�iÌ�VÃ]�Ü��V��

��Û��ÛiÃ�ÃÌÕ`Þ��}�>�`�iÛi��V��ÌÀ��}�Ã«iV�wi`�Vi��Ã�Ü�Ì��Û��}�

tissue b

y s

hin

ing

light o

n th

em

.

Figure 2 The D

SM

-5 w

as p

ub

lished

in 2

013 b

y th

e A

meric

an P

sychia

tric

Asso

cia

tion. T

his

manual fo

cuses o

n d

iag

no

sin

g a

nd

co

din

g. A

prim

ary

v�VÕÃ��v�ÛiÀÃ��wÛi�Ü>Ã�Ì��iÝ«>�`��VÕ�ÌÕÀ>��«iÀÃ«iVÌ�ÛiÃ]��Ü

iÛiÀ��iÀ�Ì��ÌÃ�ìÃ�}�]�Ì�i��

>�Õ>��ÕÌ��âiÃ�>�7iÃÌiÀ��>««À�>V��Ì��Ì��}�

ab

out m

enta

l health

.

Figure 3 Phillip

Alv

eld

a o

n th

e fu

ture

of th

e c

ortic

al m

od

em

.

Figure 5 Bra

in /

Enviro

nm

ent c

onnectio

ns a

nd

pers

pectiv

es.

Figure 6 ��Ì�iÀ�>�`�Ì�i��

�Ì��iÃ�>Ài�>�v>�

��Þ��v�Ã�>ÀÌ�

senso

rs th

at y

ou c

an s

et in

two

min

ute

s to

perfo

rm th

e fu

nctio

ns y

ou

want a

nd

that y

ou c

an c

hang

e a

s o

ften a

s y

ou n

eed

.

RESEARCH

NO

DE # 1

diagnosis • C

reatin

g a

dia

gn

ostic

, sym

pto

m a

nd

treatm

en

t pla

n

pro

toty

pe

of a

n o

nlin

e d

ata

base

/ w

eb

site

in o

rde

r

to o

pe

n a

dis

cu

ssio

n a

bo

ut h

ow

we

dia

gn

ose

an

d

to e

xp

an

d o

n W

este

rn id

eas a

bo

ut “

cu

lture

bo

un

d

ÃÞ�`À��iÃ»��Ì�i��

-��6°��Ü

�Õ�`��i�Ì��iÝ«��Ài�w

hat it m

ean

s to

be

me

nta

lly h

ealth

or m

ala

dap

tive

or

dis

ord

ere

d w

ithin

this

cu

lture

.

• Ý>�

��i��Ü�Ì�i�º>vviVÌ»��v�ìÛ�ViÃ�V�Õ�`�Ã«i>��Ì��

sym

pto

mato

log

y (fo

r exam

ple

, ho

w o

ften

do

es th

e

se

ve

rely

de

pre

sse

d p

ers

on

op

en

the

refrig

era

tor, o

r

bru

sh

the

ir tee

th).

RESEARCH

+ DESIG

N IN

SPIRATION

Ro

saly

n P

icard

: Affe

ctiv

e C

om

putin

g @

MIT

Hele

n N

issenb

aum

: Big

Data

@ N

YU

Med

ia

Sheila

Nire

nb

erg

: Pro

sth

etic

Senso

ry D

evic

es @

Co

rnell

*��«�Ƃ�Ûi�`>\�

�ÀÌ�V>��ì�

�J��}�V>��/iV��}�iÃ�"

vwVi��Ƃ,*Ƃ

®�>À��-V��Ìâ�iÀ\�

iÕÀ>��ÀVÕ�Ì��

Þ�>��VÃ�>�`�"

«Ì�V>��>}��}�J

�-Ì>�v�À`Justin

Sanchez: B

rain

Machin

e S

ym

bio

sis

/N

euro

pro

sth

etic

Researc

h @

Univ

ers

ity o

f Mia

mi

Jo

nath

an R

. Wo

lpaw

: BC

I @ W

ad

sw

orth

Cente

r

Ed

die

Chang

@ U

CSF

��Ìi��É��V�>i��°��Ý��Õ�`>Ì��*>À��Ã��½Ã�Ü

i>À>L�iB

rain

Gate

Sen.s

e

Nest

DA

RPA

RESEARCH

NO

DE # 2

data

• Ý«��Ài��ì>Ã�>L�ÕÌ�ºyÕ�`�`>Ì>»�>�`�ºyÕ�`�«À�Û>VÞ»�an

d its

rela

tion

sh

ip to

me

nta

l he

alth

.

• ��>}��i�>�vÕÌÕÀi��Ü

��V��ÕÀ��i�Ì>��i>�Ì��«À�w�i�

��ÌiÀ>VÌÃ�Ü�Ì��ÕÀ�iÝÌiÀ�>��i�Û�À��

i�ÌÃ��i�Ì�i��i]�

�vwVi�>�`�V>À]�>Ã�Üi��>Ã��ÕÀ��ÌiÀ�>��i�Û�À��

i�Ì��v�b

od

y, b

rain

an

d n

eu

ron

s.

• D

iffere

nt ty

pe

s o

f co

din

g. D

iag

no

stic

co

din

g +

co

din

g

to th

e b

rain

.

RESEARCH

NO

DE # 3

interface• A

bstra

ct th

e n

otio

n o

f ho

w w

e c

an

de

sig

n w

he

n w

e

brid

ge

the

bra

in s

yste

ms w

ith o

ur e

nviro

nm

en

ts.

• ��Ü�V>��Ü

i�ìÃ�}��v�À�Ì�i�wÀÃÌ�}i�iÀ>Ì��v�V�ÀÌ�V>��m

od

em

s?

• H

ow

can

we

de

sig

n fo

r an

d a

cco

un

t for a

ug

me

nte

d

Ài>��ÌÞ��ÕÀ�>VÌÕ>��Û�Ã��¶�,>��wV>Ì��Ã��v�

>�ÌiÀi`�Ài>��ÌÞ�Ü�Ì��ÕÀ�LÀ>��>�`��>V�i`�«i�«�i°�

ƂVV�Õ�Ì>L��ÌÞ�Ì��Ì�iÀÃ�>�`�Ã�V�iÌÞ��>V��}��Ì�iÀÃ�

to c

om

mit c

rime

s).

• H

ow

can

we

......

ºo��Ì��

i]�Üi½Ài�}��}�Ì��ìÛi��«�Ì�i�Ì��Ã�>�`�

the

tech

no

log

ies to

pe

er a

nd

write

de

ep

er a

nd

de

ep

er in

to th

e b

rain

- wh

ere

we

will e

xp

ose

an

d u

n-

de

rsta

nd

an

d b

e a

ble

to in

terfa

ce

with

yo

ur m

em

o-

ries, y

ou

r inte

nt, y

ou

r em

otio

ns. A

nd

ye

s, th

ere

are

clin

ical o

pp

ortu

nitie

s a

nd

op

po

rtun

ities fo

r go

od

LÕÌ��Ì��ÌÃ�ÛiÀÞ�ÛiÀÞ��«�ÀÌ>�Ìo

�Ì��Li�>Ü>Ài�Ì�>Ì�

the

re is

a b

ig d

iffere

nce

be

twe

en

wh

at w

e c

an

in

prin

cip

le d

o te

ch

no

log

ically

an

d w

hat w

e s

ho

uld

be

do

ing

an

d in

fact d

o o

r no

t do

”

Figure 4 Flu

id d

ata

.

Documents

Running head: PERVASIVE MENTAL HEALTH !1 · 2018. 10. 11. · PERVASIVE MENTAL HEALTH !3 Pervasive Mental Health Health and fitness wearables are increasing in popularity. IndustryARC,