BIOFEEDBACK SYSTEMS FOR STRESS REDUCTION

Towards a Bright Future for a Revitalized Field

Egon L. van den Broek

1,2,3

and Joyce H. D. M. Westerink

TNO Technical Sciences, P.O. Box 5050, 2600 GB Delft, The Netherlands

Human-Media Interaction (HMI), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands

Karakter University Center, Radboud University Medical Center Nijmegen, P.O. Box 9101,

6500 HB Nijmegen, The Netherlands

Philips Corporate Technologies, Research, Brain, Body & Behavior Group, High Tech Campus 34,

5656 AE Eindhoven, The Netherlands

Keywords:

Biofeedback, Neurofeedback, Stress, Mental healthcare, Biosignals, Closed loop systems.

Abstract:

Stress has recently been baptized as the black death of the 21st century, which illustrates its threat to current

health standards. This article proposes biofeedback systems as a means to reduce stress. A concise state-of-

the-art introduction on biofeedback systems is given. The ﬁeld of mental health informatics is introduced. A

compact state-of-the-art introduction on stress (reduction) is provided. A pragmatic solution for the pressing

societal problem of illness due to chronic stress is provided in terms of closed loop biofeedback systems. A

concise set of such biofeedback systems for stress reduction is presented. We end with the identiﬁcation of

several development phases and ethical concerns.

1 INTRODUCTION

Throughout its existence, biofeedback has been crit-

icized almost continuously (Moss and Gunkelman,

2002; Moss et al., 2004). The latest boost of criticism

and a response on it dates from around the change of

the century. Amongst many other issues of criticism,

the lack of proven efﬁcacy, the absence of standards,

and the fuzzy relation between biosignals and psycho-

logical constructs were mentioned. Therefore, to es-

tablish a solid ground for the current article, we will

deﬁne the core concepts (i.e., biosignals and biofeed-

back) at hand and denote their relations.

Physiological or biosignals can be conceptualized

as (bio)electrical signals recorded on the surface of

the body. These bio(electrical) signals are related to

ionic transport that arises as a result of electrochemi-

cal activity of cells in specialized tissue (e.g., the ner-

vous system), so-called autonomic responses. This

results in (changes in) electric currents produced by

the sum of electrical potential differences across the

tissue. This process is the same regardless of where

in the body the cells are located (e.g., the heart, mus-

cles, skin, or the brain) (S¨ornmo and Laguna, 2005).

So, biosignals reﬂect the physiological activity of a

person. This latter deﬁnition also includes both non-

electrical biosignals, such as skin temperature, and

signals obtained through invasive recording tech-

niques.

Biosignals are employed to enable biofeedback,

as the name already reveals. On May 18, 2008,

the Association for Applied Psychophysiology and

Biofeedback (AAPB), the Biofeedback Certiﬁcation

International Alliance (BCIA), and the International

Society for Neurofeedback and Research (ISNR)

jointly agreed on a standard deﬁnition of biofeedback:

Biofeedback is a process that enables an individual

to learn how to change physiological activity for

the purposes of improving health and performance.

Precise instruments measure physiological activity

such as brainwaves, heart function, breathing, muscle

activity, and skin temperature. These instruments

rapidly and accurately “feed back” information to

the user. The presentation of this information – often

in conjunction with changes in thinking, emotions,

and behavior – supports desired physiological

changes. Over time, these changes can endure

without continued use of an instrument.

For more information, see: http://www.aapb.org/,

http://www.bcia.org/, and http://www.isnr.org/.

499

L. van den Broek E. and H. D. M. Westerink J..

BIOFEEDBACK SYSTEMS FOR STRESS REDUCTION - Towards a Bright Future for a Revitalized Field.

DOI: 10.5220/0003894904990504

In Proceedings of the International Conference on Health Informatics (BSSS-2012), pages 499-504

ISBN: 978-989-8425-88-1

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

This was one of the results of a joint task force,

initiated by the AAPB and the ISNR in 2001 (Moss

and Gunkelman, 2002; Moss et al., 2004). Other re-

sults included a series of white papers and a review

of the clinical efﬁcacy of biofeedback (Moss et al.,

2004). The deﬁnitions of both biosignals and biofeed-

back provide the premises for this article. To also un-

derstand the criticism biofeedback has been subjected

to, we need to go back in time, to the invention of

biofeedback, and put the work on biofeedback into

historical perspective.

The origin of biofeedback takes us back to 1932,

the year in which Johannes Heinrich Schultz (1884−

1970) published a book on a relaxation technique

he baptized autogenic training. In 2003, the twenti-

eth edition of this book was published, which marks

its continuing inﬂuence (Schultz, 2003). However,

Schultz did not provide direct biofeedback, his tech-

nique relies on introspection, and no signals are fed

back to the user. It took more than 25 years un-

til biofeedback, as has just been deﬁned, was re-

ported (Mandler et al., 1958). They referred to it as

autonomic feedback, which they deﬁned as: the re-

lationship between autonomic response and the sub-

ject’s reported perception of such response-induced

stimulation (Mandler et al., 1958, p. 367).

The work of (Mandler et al., 1958) was conducted

more than half a century ago, in the early years of

computing machinery. At that time, computers were

invented for highly trained operators, to help them

do massive numbers of calculations (Sifakis, 2011).

However, much has changed since then; nowadays,

everybody uses them in one of their many guises.

Today we are in touch with various types of com-

puters throughout our normal daily lives, including

our smartphones (Agrawal, 2011). Computation is

on track to become even smaller and more perva-

sive. Not only computing machinery miniaturized,

biomedical apparatus has also done so (Ouwerkerk

et al., 2008). Consequently, biosignals (or physio-

logical signals) still receive increasing interest as an

interface between users and their computing devices.

It is envisioned that computers will become a window

to the world as a whole, to our social life, and even to

ourselves (Davies, 2011).

Computers are slowly becoming dressed, hug-

gable, and tangible. Concepts such as stress and

emotions, which were originally the playing ﬁeld of

philosophers, sociologists, and psychologists (Izard

et al., 2010), have already become entangled in com-

puters (and in computer science) as well. With this,

it has become much easier for us than before to ac-

cept biosignals as relevant reﬂections of our lives, and

biofeedback as a means to alter them. Also biofeed-

back standards can emerge more easily with ICT help.

Thus two of the traditional criticisms of biofeedback

are on the verge of being tackled. And while the

embedding of biofeedback into psychological con-

structs remains unclear, the ﬁrst efﬁcacy evaluations

of biofeedback have started to prove its usefulness

(Gruzelier et al., 2006).

In the next section, we will provide a concise in-

troduction on mental health informatics, as opposed

to general (physical) health informatics, with an em-

phasis on (chronic) stress. In Section 3, we will pro-

vide a concise overview of our working model for

stress reduction: a closed loop biofeedback model.

Last, in Section 4, we will provide a general discus-

sion.

2 MENTAL HEALTH

INFORMATICS AND STRESS

(REDUCTION)

In 1935, Flanders Dunbar noted that the “Scientiﬁc

study of emotion and of the bodily changes that ac-

company diverse emotional experiences marks a new

era in medicine” (Dunbar, 1954, p. vii). We know

now that many physiological processes that are of

profound signiﬁcance for health can be inﬂuenced by

way of emotions (Kaklauskas et al., 2011). For ex-

ample, it has been shown that emotions inﬂuence our

cardiovascular system and, consequently, can shorten

or prolong life (Lucas et al., 2009). Moreover,chronic

stress also plays an important role with chronic dis-

eases (Berg and Upchurch, 2007), cancer (e.g., coping

strategies) (Taylor and Stanton, 2007), and rehabilita-

tion (Novak et al., 2010), to mention three.

Flanders Dunbar drew the conclusion: “In this

knowledge, we have the key to many problems in

the prevention and treatment of illness, yet we are

scarcely begun to use what we know. We lack perspec-

tive, concerning our knowledge in this ﬁeld, and are

confused in our concepts of the interrelationship of

psychic, including emotional, and somatic processes

in health and disease.” (Dunbar, 1954, p. vii). Nev-

ertheless, emotions remained rather spiritual and hu-

man’s health has usually been explained in physical

(e.g., injuries) and physiological terms (e.g., bacte-

ria and viruses). The ﬁeld of biofeedback also suf-

fered severely from this attitude. It is only since

the last decades that it has generally been acknowl-

edged that emotions have their impact on health and

illness (Kaklauskas et al., 2011).

Now emotions have been acknowledged by tradi-

tional medicine, stress is being given a position in he-

HEALTHINF 2012 - International Conference on Health Informatics

500

alth informatics. This shift was accelerated by the

general increase in the need for health informatics that

has emerged due to the massive growth of the market

for new systems that improve productivity, cut costs,

and support the transition of health care from hospi-

tal to the home (Dumaij and Tijssen, 2011). Health

informatics is already or will soon be applied for the

support/assistance of independent living, chronic dis-

ease management, facilitation of social support, and

to bring the doctor’s ofﬁce to people’s homes. Par ex-

cellence, this is where informatics and

mental health

care

blend together.

Recently, stress has been baptized as being the

black death of the 21st century. Although this is a very

bold statement, it illustrates that (chronic) stress is an

important threat to modern societies, both in terms of

severity and in terms of proportion. As such, it is now

receiving top priority in health care and is the #1 pri-

ority in mental health care.

Humans can make cognitive representations of

events, from the past as well as for the future. This

ability distinguishes them from (most) animals. These

representations aid our daily work and living; how-

ever, they also have their down side. In stressful life

events, cognitive representations can facilitate worry-

ing and, consequently, catalyze chronic stress, which

is unknown to animal species (Brosschot, 2010).

When stress is experienced, often similar phys-

iological responses emerge as during the stressful

events from which it originates: the repetition of

such physiological responses can cause pervasive and

structural chemical imbalances in people’s physiolog-

ical systems, including their autonomic and central

nervous system, their neuroendocrine system, their

immune system, and even in their brain (Brosschot,

2010). A thorough understanding of stress is still

missing. This can be explained by the complexity of

human’s physiological systems, their continuous in-

teraction, and their integral dynamic nature.

Current day treatments of stress focuses on the

treatment of either cognitive representations, our (un-

conscious) habit memory system, or both (Schwabe

et al., 2010). In general, under stressful events, the

habit memory system tends to dominate over the cog-

nitive memory (or representations) system; however,

their precise relation remains unknown (Schwabe

et al., 2010). This lack of understanding makes treat-

ment inherently complex and requires a very high

level of expertise from the clinician. Moreover, most

indicators of the patients’ progress rely on behavior

measures and the clinician’s expertise.

A possible alternative for the clinician’s expertise

is the use of biofeedback systems for stress reduc-

tion. On the one hand, these systems have the obvious

influencing

algorithm

signal processing +

pattern recognition

Human

biosensors

(bio)feedback

actuators

machine

feedback

biosignals

Figure 1: The (general) biofeedback closed loop model. For

details on the model’s signal processing + pattern recogni-

tion component, we refer to (van den Broek et al., 2010).

Within the scope of this article, the model’s domain of ap-

plication is mental health care, in particular stress reduction.

disadvantages that they can be obtrusive, can process

only a few or even a single signal, and their means

to interact with their user are severely limited as well.

On the other hand, biofeedback systems are becom-

ing reliable and robust, affordable, and can be applied

when and where ever needed or appreciated (without

any additional costs). Moreover, biofeedback systems

for stress reduction enable an anonymous treatment

and can be (automatically) personalized. These two

aspects could be potential strengths of such systems

but could also turn out to be their weaknesses.

Taken together, both the pros and cons of biofeed-

back systems for stress reduction are strong. More-

over, stress is a complex phenomenon, which is far

from completely unraveled. Hence, these limitations

need to be respected and the claims surrounding them

should be chosen with care. For example, given the

current state of the art of science and engineering,

biofeedback systems for stress reduction might only

be employed successfully with people who do not yet

suffer severely (or already for a vast amount of time)

from stress. However, even then, biofeedback sys-

tems for stress reduction can still be useful for a large

portion and for a still increasing part of the popula-

tion.

3 THE CLOSED LOOP

To enable autonomous biofeedback systems, a closed

loop model has to be adopted, incorporating both

measurement and feedback components. Such a

BIOFEEDBACK SYSTEMS FOR STRESS REDUCTION - Towards a Bright Future for a Revitalized Field

501

model can, but does not necessarily involve the in-

tervention of a therapist. Consequently, (closed loop)

biofeedback systems can be positioned in the large

market of health care-related consumer electronics.

For over a century, closed loop models have

been known in science and engineering, in particu-

lar in control theory and electronics (Neamen, 2010).

Closed loop models can be deﬁned as control systems

with an active feedback loop. This loop allows the

control unit to dynamically compensate for deviations

in the system.

The output of the system is fed back through a

sensor measurement to a control unit, which takes the

error between a reference and the output to change the

inputs to the system under control.

A relatively new class of closed loop models are

biofeedback systems: closed loops that take a human

into the loop; see also Figure 1. The descriptions of

these biofeedback systems target various areas but are

essentially the same, comprising: sensors, processing,

inﬂuencing algorithm (feedback decision), and actua-

tors. In essence, biofeedback systems for stress re-

duction are described by four basic steps:

1. Sensing. Data collection starts at the sensors,

where a raw signal is generated that contains an

indication of a person’s mental state, e.g. his

stress level. Relevant signals can include both

overt and covert bodily signals, such as facial

camera recordings, movements, speech samples,

and biosignals.

2. Signal Processing + Pattern Recognition. Ex-

ploiting signal features that could contain stress

level information; for example, the number of

peaks in the ElectroDermal Activity (EDA) signal

can be counted, serving as a measure for stress.

For more information on this step, we refer to

(van den Broek et al., 2010).

3. Inﬂuencing Algorithm. Given the obtained af-

fective state of the user, a decision is made as to

what feedback to provide to the user. Next, we

will provide various examples of this.

4. Feedback Actuators. The feedback is provided

by a set of actuators. Such actuators can di-

rectly communicate with our body, either physi-

cally (Hatzfeld et al., 2010) or chemically (Mielle

et al., 2010). Alternatively, actuators can commu-

nicate indirectly and inﬂuence our environment as

we sense it either consciously or unconsciously;

for instance, a song can be played or lighting can

be activated to create a certain ambiance. The op-

timal way to present this feedback information is

part of the ﬁeld of Human-Computer Interaction.

The loop (always) closes with a new measurement of

the sensors, which is again evaluated as to whether

or not the intended level of stress has indeed been

reached. If the intended level of stress has indeed

been reached, the system will perform no further ac-

tion. Thus the system guides the user towards a cer-

tain state. Also in the ﬁeld of Brain-Computer Inter-

action (BCI) closed loops play an important role (van

Gerven et al., 2009). For traditional BCI devices (e.g.,

those allowing locked-in patients to communicate),

however, there is no target state included in a feed-

back algorithm, but instead it is the user who guides

the system to a certain state.

The feedback is determined by biosignals but is

most often of another modality itself. With the vast

progress of informatics, omnipresent computing (also

known as Ambient Intelligence (AmI), Ubiquitous

Computing (UbiComp), the Internet of Things, and

the World Wide Wisdom Web (W4) (Friedewald and

Raabe, 2011) are about to enter our lives. This pro-

vides a seemingly inﬁnite number of possibilities to

provide the (bio)feedback. We will mention three of

them:

1. (Ambient) Lighting: (i) The color of light can

be altered to provide the experience of another

color to people, which can either activate or re-

lax them (K¨uller et al., 2009) and (ii) The Ratio-

nalizer concept: an EDA-based emotion sensing

wristband that offers stress level feedback to stock

traders, using a LED bowl (Ouwerkerk, 2011).

2. Audio and Video: (i) The RelaxTV concept uses

a biosensor to monitor the relaxation of a person.

Biofeedback techniques were developed to use

breathing guidance for deep relaxation of a televi-

sion viewer (Ouwerkerk, 2011) and (ii) A person-

alized affective music player to augment music

experience and direct the listener’s mood (Janssen

et al., 2012), for instance to a relaxed state.

3. Tactile Feedback: (i) Research is conducted with

tactile sensation as emotion elicitor, by means of

an augmented PC mouse, which can be conve-

niently used in the context of work occupational

stress (Suk et al., 2009) and (ii) (Plasier, 2011)

introduced an electronic singing bowl. Tradi-

tional Tibetan versions of these bowls are used to

provide relaxing body vibrations along with the

sounds.

This triplet illustrates the latest developments on

biofeedback. However, many more have been pro-

posed and even more yet are currently being investi-

gated. It can be expected that the ﬁeld will mature

further and the systems will become robust. Then,

within 10 years from now, consumers can buy such

biofeedback systems in their electronics stores.

HEALTHINF 2012 - International Conference on Health Informatics

502

4 DISCUSSION

This article proposed biofeedback systems as a means

to reduce stress, which is becoming a pregnant is-

sue in our modern society. First, the necessary ba-

sic information was provided in terms of a deﬁnition

and a sketch of the origin of biofeedback systems.

Next, the ﬁeld of mental health informatics was intro-

duced followed by the article’s topic: stress (reduc-

tion). (Closed loop) biofeedback systems were intro-

duced as a feasible solution for the pressing societal

problem of illness due to chronic stress. Moreover,

several examples of biofeedback systems for stress re-

duction were provided.

Traditionally, research towards biofeedback sys-

tems has been approached from a range of sciences

(e.g., psychology, medicine, and computer science)

and often the research explores the feasibility of such

systems and has not yet actually implemented them.

However, the closed-loop model allows us to identify

the three main phases of (subsequent) development:

1. Computational modeling founded on theory, with-

out experimental validation. Such systems have

been proposed by themselves; however, in the per-

spective of biofeedback systems, such models can

be considered as the very ﬁrst phase of the actual

development.

2. Stress elicitation and measurement, with or

without classiﬁcation component. This type

of research is conducted in three environ-

ments (Healey, 2008): i) controlled laboratory re-

search; ii) semi-controlled research (e.g., as in

smart homes); and iii) ambulatory research.

3. Development of the actual biofeedback systems,

in which one can distinguish: i) the initial off-line

modeling and ii) online, real-time modeling.

This division is not as strict as it may appear; of-

ten mixtures of these three phases of development

are employed and iterations and loops are applied.

Nevertheless, it should be noted that, so far, a vast

amount of research on applied biofeedback for stress

reduction has not implemented the required closed

loop model (Janssen et al., 2012). Instead most stud-

ies present either theoretical computational modeling

or solely stress elicitation and measurement. More-

over, most research has been conducted in (semi-)

controlled settings. And even though ambulatory re-

search with loose constraints, conducted in the real

world, is still relatively rare (Healey, 2008) (cq.

(Janssen et al., 2012)), we do expect a rise of closed

loop biofeedback systems for stress reduction.

Building biofeedback systems for stress reduction

is a process in which law and ethics will claim their

place too (Floridi, 2010). Law considerations com-

prise: i) rules of privacy, ii) the constitutional back-

ground, and iii) privacy under law, including physi-

cal, decisional, and information privacy. Ethical con-

siderations emerge from the notion that biofeedback

systems would extend the scope of traditional infor-

mation collection. One of the ethical issues is that

biofeedback systems may introduce the risk of so-

cial exclusion for those who do use them, or maybe

even for those who do not. This makes the balance

between intelligence (e.g., AmI) and privacy even

more sensitive than, for example, with biometrics.

Taken together, perhaps more than anything else, hu-

man dignity should be a leading denominator in fu-

ture research on biofeedback systems for stress reduc-

tion (Coeckelbergh, 2011).

Stress is heading to become the #1 in (chronic)

diseases. As such its societal impact is enormous.

With this article we hope to bring the need for a solu-

tion to the attention of the health informatics society.

Closed loop biofeedback systems for stress reduction

are proposed as a feasible solution and examples of

such systems have been described. Taken together,

we propose to increase the efforts towards biofeed-

back systems for stress reduction, if not for the sake

of science then at least for the sake of society.

ACKNOWLEDGEMENTS

We are grateful to the ﬁve reviewers for their com-

ments on an earlier draft of this article. We thank

Lynn Packwood (Human Media Interaction, Univer-

sity of Twente, NL) for her accurate proof reading.

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