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Multimodal Interaction: An Introduction


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A 1-hour introductory lecture on multimodal interaction that I gave to bachelor HCI students. Included a section on how to get started in this exciting line of research.

A 1-hour introductory lecture on multimodal interaction that I gave to bachelor HCI students. Included a section on how to get started in this exciting line of research.

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  • 1. Multimodal Interaction! An Introduction! Abdallah  ‘Abdo’  El  Ali   h"p://   Some slides adapted from: Gabriel Skantze (KTH Royal Institute of Technology, Sweden), Denis Lalanne (University of Fribourg, Switzerland)
  • 2. 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
  • 3. Outline! I.  Mul=modal  Interac=on  &  Interfaces   II.  Mul=modal  Input   III.  Mul=modal  Output   IV.  Prac=cal  Ma"ers    3
  • 4. Multimodal Interaction & Interfaces!4
  • 5. 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
  • 6. Multimodal Interfaces!6
  • 7. Project NATAL for Xbox 360 Playstation EyePet7 Kinect for Xbox 360 Playstation Move
  • 8. 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
  • 9. 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
  • 10. 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
  • 11. 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
  • 12. 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
  • 13. 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
  • 14. 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
  • 15. 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
  • 16. Early Example! “Put  That  There”  system     (Bolt,  1980)   Speech  and  gestures  used  simultaneously  16
  • 17. 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
  • 18. 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
  • 19. Questions?!19
  • 20. Multimodal Input!20
  • 21. 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
  • 22. 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
  • 23. 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
  • 24. 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
  • 25. Multimodal Man-Machine Interaction Model!25 (Dumas et al., 2009)
  • 26. 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
  • 27. Unimodal or Multimodal?!27
  • 28. 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
  • 29. NUMACK (Foster and White, 2005)!   NUMACK  (Northwestern  University   Mul=modal  Autonomous  Conversa=onal   Kiosk)     Embodied  Conversa=on  Agent  (ECA)  that   gives  direc=ons  around  Northwesterns   Campus     Combina=on  of  speech,  gestures  and  facial   expressions     Uses  a  grammar-­‐based,  computa=onal  model   of  language  and  gesture  planning  system     NUMACKs  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
  • 30. 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
  • 31. Questions?!31
  • 32. Multimodal Output!32
  • 33. 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
  • 34.     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
  • 35. 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:   Pocket  Navigator  (Pielot  et  al,  2010)   AudioGPS  (Holland  et  al.,  2002)    35
  • 36. 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
  • 37. 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
  • 38. 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
  • 39. Questions?!39
  • 40. Practical Matters !40
  • 41. 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
  • 42. 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
  • 43. 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
  • 44. 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
  • 45. 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
  • 46. The Future of Computing is Multimodal…!    46
  • 47. Contact! Abdo  El  Ali   e:   w:  h"p://   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://
  • 48. References (1)! Bla"ner,  M.  M.,  Sumikawa,  D.  A.,  &  Greenberg,  R.  M.  (1989).  Earcons  and  icons:  Their  structure  and  common  design   principles.  Human-­‐Computer  Interac=on,  4,  1,  11-­‐44   Bolt.,  R.  A.  (1980).  “Put-­‐that-­‐there”:  Voice  and  gesture  at  the  graphics  interface.  SIGGRAPH  Comput.  Graph.  14,  3,   262-­‐270.   Brown,  L.  M.,  Brewster,  S.  A.  and  Purchase,  H.  C.  (2005).  A  First  Inves=ga=on  into  the  Effec=veness  of  Tactons.  In   Proceedings  of  the  First  Joint  Eurohap=cs  Conference  and  Symposium  on  Hap=c  Interfaces  for  Virtual  Environment   and  Teleoperator  Systems  (WHC  05).  IEEE  Computer  Society,  Washington,  DC,  USA,  167-­‐176.   Brewster,  S.,  Lumsden,  J.,  Bell,  M.,  Hall,  M.,  and  Tasker,  S.  (2003.)  Mul=modal  eyes-­‐free’  interac=on  techniques  for   wearable  devices.  In  Proc.  of  CHI  03.  ACM  Press,  New  York,  NY.   Buxton,  W.  (1986)  Theres  More  to  Interac=on  than  Meets  the  Eye:  Some  Issues  in  Manual  Input.  In  Norman,  D.  A.  and   Draper,  S.  W.  (Eds.),  (1986),  User  Centered  System  Design:  New  Perspec=ves  on  Human-­‐Computer  Interac=on.   Lawrence  Erlbaum  Associates,  Hillsdale,  New  Jersey,  pp.  319-­‐337.   Chi"aro,  L.  (2009).  Dis=nc=ve  aspects  of  mobile  interac=on  and  their  implica=ons  for  the  design  of  mul=modal   interfaces.  Journal  on  Mul=modal  User  Interfaces,  3(3),  157-­‐165.   Dourish,  P.  (2000).  Embodied  Interac=on:  Exploring  the  Founda=ons  of  a  New  Approach  to  HCI.  Transac=ons  on   Computer-­‐Human  Interac=on.   Dumas,  B.,  Lalanne,  D.  and  Ovia",  S.  (2009).  Mul=modal  Interfaces:  A  Survey  of  Principles,  Models  and  Frameworks.  In   Human  Machine  Interac=on,  Denis  Lalanne  and  Jorg  Kohlas  (Eds.).  Lecture  Notes  In  Computer  Science,  Vol.  5440.   Springer-­‐Verlag,  Berlin,  Heidelberg  3-­‐26.   Gibson,  J.  J.  (1966).  The  Senses  Considered  as  Perceptual  Systems.  Houghton  Mifflin,  Boston.   Gibson,  J.  J.  (1979).  The  Ecological  Approach  to  Visual  Percep=on.  Houghton  Mifflin,  Boston.   Heidegger,  M.  (1927).  Being  and  Time.  Trans.  by  John  Macquarrie  &  Edward  Robinson,  London:  SCM  Press,  1962).   Hoggan,  E.  and  Brewster,  S.A.  (2007)  Designing  Audio  and  Tac=le  Crossmodal  Icons  for  Mobile  Devices.  In  ACM   Interna=onal  Conference  on  Mul=modal  Interfaces  (Nagoya,  Japan).  ACM  Press,  pp  162-­‐169  48
  • 49. References (2)! Hoggan,  E.,  Raisamo,  R.  and  Brewster,  S.A  (2009).  Mapping  Informa=on  to  Audio  and  Tac=le  Icons.  In   Proceedings  of  ACM  ICMI  2009  (Cambridge,  MA,  USA).  ACM  Press,  pp  327-­‐334     Holland,  S.,  Morse,  D.  R.,  and  Gedenryd,  H.  (2002).  AudioGPS:  Spa=al  audio  naviga=on  with  a  minimal  a"en=on   interface.  Personal  Ubiquitous  Comput.,  6(4):253–259,  2002   Kopp,  S.,  Tepper,  P.  and  Cassell,  J.  (2004).  "Towards  Integrated  Microplanning  of  Language  and  Iconic  Gesture  for   Mul=modal  Output.“  ICMI  2004.         Lewkowicz,  D.  J.  (1994).  Development  of  intersensory  percep=on  in  human  infants.  In  Lewkowicz,  D.  J.  &   Lickliter,  R.  (Eds.).  Development  of  Intersensory  Percep=on:  Compara=ve  Perspec=ves,  Norwood,  N.J.:   Lawrence  Erlbaum  Associates     Magnusson,  C.,  Tollmar,  K.,  Brewster,  S.,  Sarjakoski,  T.,  Sarjakoski,  T.,  &  Roselier,  S.  (2009).  Exploring  future   challenges  for  hap=c,  audio  and  visual  interfaces  for  mobile  maps  and  loca=on  based  services.  In  Proceedings   of  the  2nd  interna=onal  workshop  on  loca=on  and  the  web  (pp.  8:1{8:4).  New  York,  NY,  USA:  ACM.   Nigay,  L.  and  Coutaz,  J.  (1993).  A  design  space  for  mul=modal  systems:  concurrent  processing  and  data  fusion.   In  Proceedings  of  the  INTERACT  93  and  CHI  93  conference  on  Human  factors  in  compu=ng  systems  (CHI  93).   ACM,  New  York,  NY,  USA,  172-­‐178.   Pielot,  M.,  Krull,  O.  and  Boll,  S.  (2010b).  Where  is  my  team:  suppor=ng  situa=on  awareness  with  tac=le  displays.   In  Proceedings  of  the  28th  interna0onal  conference  on  Human  factors  in  compu0ng  systems  (CHI  10).  ACM,   New  York,  NY,  USA,  1705-­‐1714.   Pielot,  M,  Poppinga,  B.,  and  Boll,  S.  (2010).  PocketNavigator:  Vibro-­‐Tac=le  Waypoint  Naviga=on  for  Everyday   Mobile  Devices,  Mobile  HCI  2010,  Lisboa,  Portugal.   Reeves,  L.  M.,  KLai,  J.,  Larson,  J.  A.,  Ovia",  S.,  Balaji,  T.  S.,  Buisine,  S.,  Collings,P.,  Kraal,  B.,  Mar=n,  J.  C.,  McTear,   M.,  Raman,  T.  V.,  Stanney,  K.  M.,  Su,  H.,  and  Wang,  Q.  Y.  Guidelines  for  Mul=modal  User  Interface  Design.   Commun.  ACM  47(1)(2004),  57  –  59.   Visell.  Y.  (2009).  Tac=le  sensory  subs=tu=on:  Models  for  enac=on  in  HCI.  Interact.  Comput.  21,  1-­‐2,  p.38-­‐53.    49