Multimodal Interaction!
An Introduction!
Abdallah
‘Abdo’
El
Ali
h"p://staff.science.uva.nl/~elali/
Some slides adapted from:
Gabriel Skantze (KTH Royal Institute of Technology, Sweden),
Denis Lalanne (University of Fribourg, Switzerland)

Who am I?!
 Currently:
PhD
in
Mobile
Human-­‐Computer
Interac<on
-­‐UvA
 Crossmodal
Interac=on
in
Mobile
Environments
 Msc
in
Cogni<ve
Science
-­‐
UvA
 Cogni=on,
Language,
&
Communica=on
track
 Bsc
in
English
Language
&
Literature
-­‐
American
University
of
Beirut
 Screenwri=ng,
Copywri=ng,
Edi=ng
2

Outline!
I. Mul=modal
Interac=on
&
Interfaces
II. Mul=modal
Input
III. Mul=modal
Output
IV. Prac=cal
Ma"ers
3

Multimodal Interaction & Interfaces!
4

A Brief History of Computer Interfaces!
 Punched
cards
(late
19th
century)
 Herman
Hollerith
-­‐
Tabula=ng
Machine
Company
(1896)
 The
Command
Line
Interface
(1960s)
 Sketchpad
(1963)
by
Ivan
Sutherland
–
light-­‐pen
pointer-­‐based
system
to
create
and
manipulate
objects
in
drawings
 Alto
personal
computer
(1973)
developed
at
Xerox
PARC
 Desktop
metaphor,
WIMP
(windows,
icons,
menus,
poin=ng
device)
 WYSIWYG
 Xerox
8010
Star
Informa=on
System
(1981)
 Apple
Macintosh
(1984)
 Windows
1.01
(1987)
 Microsoc
Windows
3.0
(1990)
 Mac
OSX
(2000’s)
 […]
5

Multimodal Interfaces!
6

Project NATAL for Xbox 360
Playstation EyePet
7 Kinect for Xbox 360 Playstation Move

HCI and Human Characteristics !
 HCI
is
a
mul=-­‐disciplinary
topic
 Computer
Science
&
AI
 Cogni=ve
Science
 Sociology
 Psychology
 Design
 […]
 In
HCI
design,
important
to
understand
something
about
 Human
informa=on-­‐processing
(cogni=ve
architecture,
memory,
percep=on,
motor
skills,
etc.)
 How
human
ac=on
is
structured
 The
nature
of
human
communica=on
 Human
physical
and
physiological
requirements/constraints
8

Why HCI?!
 Humans
are
limited
in
their
capacity
to
process
informa=on
 Implica=ons
for
the
interac=on
design
 Mul=tasking
says
it
all
 Important
considera=ons
 Input-­‐output
channels
(senses
and
effectors)
 Memory
 Learning
(acquiring
skills)
 Reasoning
/
Problem
solving
(cogni=ve
ac=vity)
 Decision
making
9

Use Case: Mobile Interaction!
Dis=nc=ve
aspects
of
mobile
interac=on
(Chi"aro,
2010):
 Hardware:
small
screen,
limited
I/O
 Perceptual:
noisy
street,
sunlight
reflec=on,
no
device
contact
 Motor:
voluntary
movements
when
in-­‐
vehicle,
fat-­‐finger
problem
 Social:
phone
ring
at
a
conference,
gestures
in
front
of
strangers
 Cogni<ve:
limited
a"en=on
span,
high
stress
&
load,
limited
memory
10

Embodiment!
 Embodied
cogni=on,
Situated
Cogni=on,
Embodied
Interac=on,
EEC,
Social
Compu=ng,
Tangible
Compu=ng,
Ac=ve
percep=on,
[…]
 Gibson
(1979)
“ The
Ecological
Approach
to
Visual
Percep=on”
 “....perceiving
is
an
act
not
a
response,
an
act
of
a"en=on,
not
a
triggered
impression,
an
achievement,
not
a
reflex”
 Heidegger
(1927)
“Being
and
Time”
 Present-­‐at-­‐hand
vs.
ready-­‐to-­‐hand
 e.g.,
hammer
as
object
(presence)
vs.
hammer
as
tool
(cogni=ve
extension)
 E.g.,
mouse
as
hardware
vs.
mouse
as
tool
for
performing
GUI
opera=ons
 Dourish
(1999)
“Founda=ons
of
Embodied
Interac=on”
 “…interac=on
is
an
embodied
phenomenon.
It
happens
in
the
world,
and
that
world
(a
physical
world
and
a
social
world)
lends
form,
substance
and
meaning
to
the
interac=on.
 Sensori-­‐motor
coordina=on
 Percep=on
for
ac=on
Agent
 Ac=on
for
percep=on
World

Sensation & Perception!
 Humans
perceive
the
world
through
their
senses
(sensory
input)
and
act
on
it
through
the
motor
control
of
their
effectors
 Five
major
senses
 Sight
 Hearing
 Touch
 Taste
 Smell
 (Propriocep=on,
thermocep=on,
nociocep=on,
…)
 Effectors
 Limbs
(arms,
legs,
body
posi=on,
…)
 Fingers
 Eyes
 Head
/
Face
 Body
 Vocal
system
12

Man-Machine Interaction!
 Interac<on
can
be
seen
as
a
dialog
between
the
computer
and
the
user
 Interac=on
styles
:
 Command
language
/
Command
line
interface
 Form-­‐fills
and
spreadsheets
 Menus
 Natural
language
and
query
language
 Ques=on/answer
dialog
 WIMP
 Point-­‐and-­‐click
 Direct
manipula=on
 3D
interfaces
(virtual
reality)
 Brain-­‐computer
interface
13

Multimodal Interfaces!
Mul<modal
Interac<on:
the
situa=on
where
the
user
is
provided
with
mul=ple
modes
for
interac=ng
with
a
system
Mul<modal
Interfaces
“…process
two
or
more
combined
user
input
modes
(such
as
speech,
pen,
touch,
manual
gesture,
gaze,
and
head
and
body
movements)
in
a
coordinated
manner
with
mul=media
system
output.
They
are
a
new
class
of
interfaces
that
aim
to
recognize
naturally
occurring
forms
of
human
language
and
behavior,
and
which
incorporate
one
or
more
recogni=on-­‐
based
technologies
(e.g.
speech,
pen,
vision)”
(Ovia"
et
al.,
2002)
14

Multimodality vs. Multimedia!
 Modality
“refers
to
the
type
of
communica=on
channel
used
to
convey
or
acquire
informa=on.
It
also
covers
the
way
an
idea
is
expressed
or
perceived,
or
the
manner
an
ac=on
is
performed”
(Nigay
&
Coutaz,
1993)
 Visual,
Auditory,
Hap=c,
etc.
 Mul=-­‐
refers
to
2
or
more
such
modali=es
used
 Mode
“refers
to
a
state
that
determines
the
way
informa=on
is
interpreted
to
extract
or
convey
meaning”
(Nigay
&
Coutaz,
1993)
 Mul<media
“focuses
on
the
medium
or
technology
rather
than
the
applica0on
or
user”
(Buxton,
1986)
 e.g.,
sound
clip
a"ached
to
a
presenta=on
 Media
channels:
Text,
graphics,
anima=on,
video,
etc.
15

Early Example!
“Put
That
There”
system
(Bolt,
1980)
Speech
and
gestures
used
simultaneously
16

Why Multimodal Interaction?!
Advantages
over
GUI
and
unimodal
systems:
 Natural/realism:
making
use
of
more
(appropriate)
senses
 New
ways
of
interac=ng
 Flexible:
different
modali=es
excel
at
different
tasks
 Wearable
computers
and
small
devices
 e.g.,
keyboard
typing
devices
require
training
 Helps
the
visually/physically
impaired
 Faster,
more
efficient,
higher
informa=on
processing
bandwidth
 Robust:
mutual
disambigua=on
of
recogni=on
errors
 Mul=modal
interfaces
are
more
engaging
17

Why Multimodal Interaction?!
Human
–
Human
protocols
Ini0a0ng
conversa0on,
turn-­‐taking,
interrup0ng,
direc0ng
a:en0on,
…
Human
–
Computer
protocols
Shell
interac0on,
drag-­‐and-­‐
drop,
dialog
boxes,
…
 Use more of users’ senses
 Users perceive multiple things at once
 Users do multiple things at once (e.g., speak and use
hand gestures, body position, orientation, and gaze)
18

Questions?!
19

Multimodal Input!
20

Multimodal Input Overview!
Mul=modal
Input:
 allows
humans
to
communicate
naturally
 provides
user
with
mul=ple
input
modali=es
 permits
mul=ple
styles
of
interac=on
 may
be
simultaneous
or
not
 must
consider
modality
fusion
and
temporal
constraints
21

Multimodal Input!
 Poin=ng
(deixis),
(Mul=-­‐)Touch
 Mo=on
controller
 Accelerometer,
gyro
 Speech
 Free
form,
fixed,
non-­‐speech
sounds
 Body
movement/Gestures
 Gait,
posture
 Head
posi=on
&
movements
 Facial
expression,
Gaze
 Tangibles
 Digital
pen
and
paper
 Biometrics
 Sweat,
pulse,
respira=on,
skin
conductance
 Brain
ac=vity
(neural)
 EEG
signals,
fMRI
signals,
blood
oxygena=on
 Scent?
 Odor
detec=on

Taste?
22

Speech and Gesture Interaction!
 Speech
 User
sa=sfac=on
is
highly
dependant
on
their
profiles
and
tasks
 The
learning
rate
is
fast
 Error
handling
is
getng
be"er
 Perceptual
&
social
usage
constraints
are
important
(ambient
noise,
confiden=ality,
disturbance,
etc.)
 Good
spoken
languages:
short
sentences
with
prosody
clearly
demarca=ng
end
of
words

Gesture

Habits
are
inherited
from
the
usage
of
mouse

Gesture
poin=ng
is
direct
and
reliable
(deixis)

Gesture
signs
may
not
be
natural
making
recogni=on
hard
23

Fundamental Problems !
 Aligning
HCI
tasks
with
modali<es
(and
vice
versa)
 Aligning
mul=modal
usage
to
user
profiles
(and
vice
versa)
 Mul<modal
Fusion
 the
integra=on
of
communica=on
modali=es
in
interac=ve
systems

Input
 Mul<modal
Fission
 the
repar==oning
of
informa=on
among
several
communica=on
modali=es

Output
24

Multimodal Man-Machine Interaction Model!
25 (Dumas et al., 2009)

Levels of Multimodal Fusion!
Data
Level:
e.g.,
combining
2
webcam
video
streams,
mul=ple
perspec=ves
Feature
level:
e.g.,
combining
speech
and
lip
movements
Decision
level:
e.g.,
combining
gestures
and
speech
26

Unimodal or Multimodal?!
27

MATCH: Multimodal Access to City Help
(Johnston et al., 2002)!
 Interac=ve
city
guide
and
naviga=on
applica=on:
provides
restaurant
and
subway
informa=on
for
NY
and
DC
 Dynamic
map-­‐based
interface
on
tablet
 Input
modali=es:
 Speech,
pen
gesture,
handwri=ng,
GUI
 Commands
can
be
speech,
pen,
or
mul=modal
 Visual
parsing
of
complex
gestural
input
 Output
modali=es:
 Coordinated
mul=modal
output
combining
synthe=c
speech
and
dynamic
graphics
 Example:
 Speech:
“show
inexpensive
italian
places
in
chelsea”
 Mul=modal:
“cheap
italian
places
in
this
area”
(pen
gesture;
right)
28

NUMACK (Foster and White, 2005)!
 NUMACK
(Northwestern
University
Mul=modal
Autonomous
Conversa=onal
Kiosk)
 Embodied
Conversa=on
Agent
(ECA)
that
gives
direc=ons
around
Northwestern's
Campus
 Combina=on
of
speech,
gestures
and
facial
expressions
 Uses
a
grammar-­‐based,
computa=onal
model
of
language
and
gesture
planning
system
 NUMACK's
verbal,
non-­‐verbal
and
mul=modal
behaviors
realized
through
synthesized
speech
and
kinema=c
body
model
 System
updates
its
model
of
context
and
the
world
by
fusing
mul=modal
user
input
 Stereoscopic,
head-­‐tracking
system
 Speech
 Pen
29

Multimodal Input Advantages!
 Improved
error
handling
&
efficiency
 fewer
errors
 faster
task
comple=on
 Greater
expressive
power
 Greater
precision
in
visual-­‐spa=al
tasks
(e.g.,
map
scrolling
&
item
localiza=on)
 Support
for
users’
preferred
interac=on
style
 Accommoda=on
to
diverse
users,
tasks
&
usage
environments
 e.g.,
accented
speakers
&
mobile
environments
 Shorter
&
less
complex
linguis=c
construc=ons
 e.g.,
fewer
loca=ve
descrip=ons
30

Questions?!
31

Multimodal Output!
32

Multimodal Output!
 Visual
 Text
 Graphics
 Anima=ons
 Virtual/Augmented
Reality
 Auditory
 Speech
(e.g.,
Embodied
Conversa=onal
Agent)
 Non-­‐speech
Sound
 Hap=cs
(tac=le)
 Force
feedback
(e.g.,
PS3
controller)
 Vibrotac=le
(e.g.,
phone
vibrate)
 Scent?
 Scented
mobile
phones
 Taste?
33

Multimodal Output!
 Advantages
(Sarter,
2006;
Ovia",
2002):
 Synergy
 Redundancy
 Higher
Informa=on
bandwidth
 Wicken’s
Mul=ple
Resource
Theory
(1984)
 More
modali=es
=
be"er?
 Higher
resource
compe==on
when
people
have
to
a"end
to
two
sources
at
once
(Reeves
et
al.,
2004).
34

Mobile Multimodal Interfaces!
 Mobile
context
means
a"en=onal
and
memory
resources
are
limited
(Tamminen
et
al.,
2004)
 E.g.,
map
scrolling,
talking
with
friend,
crossing
the
street
 Poten=al
of
mul=modal
feedback
cues
in:
1. addressing
issues
of
accessibility
(e.g.,
to
support
blind
users
in
naviga=on)
(Magnusson
et
al.,
2009)
2. developing
pedestrian
naviga=on
aids
to
support
situa=onal
impairment
and
awareness
(Brewster
et
al.,
2003)
 Examples:
http://www.lalyagaye.com/
 Pocket
Navigator
(Pielot
et
al,
2010)
http://feelspace.cogsci.uni-osnabrueck.de/
 AudioGPS
(Holland
et
al.,
2002)
35

Tactile and Non-Speech Auditory Feedback!
 Tactons:
“Structured,
abstract
messages
that
can
be
used
to
communicate
non-­‐
visually”
(Brown,
2005).
Informa=on
encoded
in
parameters
such
as:
 Waveform,
dura=on,
rhythm,
spa=al
loca=on,
frequency,
[…]
 Earcons:
“Non-­‐verbal
audio
messages
that
are
used
in
the
computer/user
interface
to
provide
informa0on
to
the
user
about
some
computer
object,
opera0on
or
interac0on"
(Bla"ner,
1989).
Informa=on
encoded
in:
 Pitch,
amplitude,
dura=on,
spa=al
loca=on,
[…]
 Amodal
parameters:
consist
of
informa=on
that
is
not
specific
to
any
one
sensory
modality
(Lewkowickz,
1994).
Parameters
common
to
both
tac=le
and
auditory
domains
(Lewkowickz,
1994;
Hoggan
et
al.,
2009):
 Spa=al
loca=on,
rhythm,
texture,
dura=on,
frequency,
intensity/amplitude
36

Crossmodal Interaction!
 Subset
of
mul=modal
interac=on
where
the
senses
receive
the
‘same’
informa=on
content
across
invoked
sensory
modali=es
(Gibson,
1966;
Lewkowicz,1994)
 Cf.,
Sensory
Subs=tu=on
(Visell,
2008)
 vOICe:
Seeing
with
Sound
applica=on;
Braille
 Crossmodal
Interac=on
refers
to
situa=ons
where
characteris=cs
of
one
sensory
modality
may
be
bi-­‐direc=onally
transformed
into
the
characteris=cs
of
another
(e.g.,
audio
⇿
tac=le)
(Hoggan,
2007;
2009)

Redundancy
37

Crossmodal Output Advantages!
 Crossmodal
output
advantages:
 Unlike
mul=modal
interac=on,
li"le
risk
of
informa=on
processing
overload
 When
one
sensory
modality
is
knocked
out
(e.g.,
noise
environment,
body
contact),
informa=on
is
s=ll
received
 Permits
both
‘eyes-­‐free’
and
‘hands-­‐free’
interac=on
38

Questions?!
39

Practical Matters !
40

Multimodal Input Research Areas!
 Applied
Machine
Learning
 Speech
Recogni=on,
Speech
Synthesis
 Gesture
Recogni=on,
Mo=on
Tracking
 Head,
Gait
and
Pose
Es=ma=on
 Mul=modal
Fusion

HCI
 Usability
issues
in
diverse
tasks
 Social
acceptability
 Context-­‐aware
and
ubiquitous
compu=ng
(which
modality
to
use
when)
 Design/Prototyping
of
Mul=modal
Interfaces
(e.g.,
wizard
of
Oz)
41

Multimodal Output Research Areas!
 Virtual
and
Mixed
Reality
(Immersive
Environments)
 Embodied
Conversa=on
Agents
 Hap=cs:
force-­‐feedback,
vibrotac=le
feedback
 Audio:
feedback,
synthesis
 Crossmodal
Integra=on
 HCI
(Usability,
Ssa<sfac<on)
 Mul=modal
Feedback
(in-­‐vehicle/pedestrian
naviga=on,
safety
and
control,
surgery,
ergonomics,
etc.)
 Crossmodal
Feedback
 (Mobile)
Mul=modal
Interface
design
42

International Communities!
 CHI:
ACM
CHI
Conference
on
Human
Factors
in
Compu=ng
Systems
 MobileHCI:
ACM
conference
on
Human-­‐computer
interac=on
with
mobile
devices
and
services
 ICMI:
ACM
Interna=onal
Conference
on
Mul=modal
Interac=on

CSCW:
ACM
Conference
on
Computer
Supported
Coopera=ve
Work
 ACM
MM:
ACM
Mul=media
Conference
 INTERACT:
IFIP
conference
on
Human-­‐Computer
Interac=on
 WHC:
World
Hap=cs
Conference
43

Resources!
 Books:
 Paul
Dourish
(2004)
“Where
the
Ac=on
is:
The
founda=ons
of
embodied
interac=on”
 Andy
Clark
(2003)
“Natural-­‐Born
Cyborgs:
Minds,
Technologies,
and
the
Future
of
Human
Intelligence”
 Bill
Buxton
(2007)
“Sketching
User
Experiences:
Getng
the
design
right
and
the
right
design”
 Adam
Greenfield
(2006)
“Everyware:
The
dawning
age
of
ubiquitous
compu=ng”
 Ar<cles:

Mark
Weiser
(1991)
“ The
Computer
for
the
21st
Century”,
Scien0fic
American
 Sharon
Ovia"
(2002)
“Perceptual
user
interfaces:
mul=modal
interfaces
that
process
what
comes
naturally”,
Communica=ons
of
the
ACM
 Sharon
Ovia"
(1999)
“ Ten
myths
of
mul=modal
interac=on”,
Communica=ons
of
the
ACM
 Nadine
Sarter
(2006)
“Mul=modal
informa=on
presenta=on:
Design
guidance
and
research
challenges”,
Interna=onal
Journal
of
Industrial
Ergonomics
 Leah
Reeves
et
al.
(2004)
“Guidelines
for
mul=modal
user
interface
design”,
Communica=ons
of
the
ACM
44

Summary!
 We
are
embodied
and
embedded
creatures,
and
this
influences
the
way
we
interact
with
the
world
and
computa=onal
ar=facts
 Mul<modal
Interfaces
aim
at
making
communica=on
with
machines
more
natural,
more
efficient,
and
more
engaging

Mul<modal
Input
and
Output
focus
on
different
aspects
within
HCI,
requiring
different
skill
sets,
but
mul=modal
research
and
development
requires
both
 Mul<modal
Interac<on
is
an
exci=ng
and
rapidly
growing
area
that
hugely
benefits
from
HCI
work
45

The Future of Computing is Multimodal…!
46

Contact!
Abdo
El
Ali
e:
elali@uva.nl
w:
h"p://staff.science.uva.nl/~elali/
t:
+31
(0)20
525
8661
Address:
Room
C3.258,
Informa=cs
Ins=tute,
Science
Park
904,
1098
XH
Amsterdam,
NL
47 Slides
available
at:
h"p://staff.science.uva.nl/~elali/hci_abdo_2011.pdf

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49