The Generalized Perceived Input Point Model and How to Double Touch Accuracy by Extracting Fingerprints

The Generalized Perceived Input Point Model and How to Double Touch Accuracy by Extracting Fingerprints

It is generally assumed that touch input cannot be accurate because of the fat finger problem, i.e., the softness of the fingertip combined with the occlusion of the target by the finger. In this paper, we show that this is not the case. We base our argument on a new model of touch inaccuracy. Our model is not based on the fat finger problem, but on the perceived input point model. In its published form, this model states that touch screens report touch location at an offset from the intended target. We generalize this model so that it represents offsets for individual finger postures and users. We thereby switch from the traditional 2D model of touch to a model that considers touch a phenomenon in 3-space. We report a user study, in which the generalized model explained 67% of the touch inaccuracy that was previously attributed to the fat finger problem.

In the second half of this paper, we present two devices that exploit the new model in order to improve touch accuracy. Both model touch on per-posture and per-user basis in order to increase accuracy by applying respective offsets. Our RidgePad prototype extracts posture and user ID from the user’s fingerprint during each touch interaction. In a user study, it achieved 1.8 times higher accuracy than a simulated capacitive baseline condition. A prototype based on optical tracking achieved even 3.3 times higher accuracy. The increase in accuracy can be used to make touch interfaces more reliable, to pack up to 3.3^2 > 10 times more controls into the same surface, or to bring touch input to very small mobile devices.

More information on http://www.christianholz.net/generalized_perceived_input_point_model.html

1b1d5420dfff84bf061b2cc53e9b839b?s=128

Christian Holz

April 12, 2010
Tweet

Transcript

  1. The Generalized Perceived Input Point Model and How to Double

    Touch Accuracy by Extracting Fingerprints christian holz | patrick baudisch
  2. fat nger

  3. fat nger

  4. so touch is inaccurate or is it?

  5. could it be that it is not the ngers but

    our touch devices that are wrong?
  6. Part 1 (science): even though screens are 2D, pointing is

    not Part 2 (engineering): sensing ngers in 3D ! highly accurate touch
  7. no fat nger we claim there is problem

  8. perceived instead, almost all observed targeting error comes from problem

    input point
  9. perceived input point problem target [Vogel&Baudisch 2007]

  10. perceived input point problem target [Vogel&Baudisch 2007] touch device perceives

  11. why we hope it’s the perceived input point problem?

  12. o set why we hope it’s the perceived input point

    problem?
  13. o set why we hope it’s the perceived input point

    problem? we can correct for it
  14. o set why we hope it’s the perceived input point

    problem? the fat nger problem, in contrast is always noise = error we can correct for it
  15. while there is always an o set, we hypothesize that

    the o set depends on the pointing situation our main hypothesis
  16. so what does “pointing situation” mean?

  17. ≠ [iPhone, Wang et al. ’09] 1yaw

  18. ≠ [Forlines et al. CHI’07] 2pitch

  19. ≠ 3roll

  20. ≠ 4users: nger shape

  21. ≠ target target 4users: mental model

  22. (… and there might be more e.g., head position/parallax…)

  23. a non 2D-model

  24. x/y current model screen

  25. x/y x/y current model touch pad screen

  26. nD touch pad screen proposed model x/y

  27. user study 1

  28. task

  29. 2. acquire target precisely 1. push okay task

  30. footswitch on-screen instructions controlled head position ! parallax capacitive touch

    pad
  31. 1 yaw

  32. !"# $%# &%# "# '&%# 2roll pitch constant

  33. 3pitch !"# (%# $%# )%# &%# roll constant

  34. 4user 12 participants all students, so di erences among them

    will be lower bound
  35. *+*,-./,012.!"#$!%"%&'(&'&%$)&1/./3*./4563.2461748 dependent

  36. *+*,-./,012.!"#$!%"%&'(&'&%$)&1/./3*./4563.2461748 dependent

  37. *+*,-./,012.!"#$!%"%&'(&'&%$)&1/./3*./4563.2461748 dependent 648/1089.!%: 4;.122.91<=2*9>

  38. main e ects for roll, pitch, yaw, & participant ID

    hypotheses
  39. if the additional IVs had no impact, we would expect

    to see something like this
  40. or touch locations indeed fall into clusters…

  41. 2 yaw × 2 sessions (pitch, roll) × 5 angles

    × 6 repetitions per angle × 5 blocks = 600 trials / participant 12 participants design
  42. results

  43. !"" #"" $"" %"" &"" '!"" '#"" '$"" '%"" '&""

    ()*+,(,-.*/0(-1230,.41( 45)0165) 45)0)-//:4,(23 45)0165)90)-//:4,(23 45)07*8 45)0*// 45)07*890165) 45)07*890)-//:4,(23 ?5@48.90A*.;4,.!%:.1665,16- results
  44. !"" #"" $"" %"" &"" '!"" '#"" '$"" '%"" '&""

    ()*+,(,-.*/0(-1230,.41( 45)0165) 45)0)-//:4,(23 45)0165)90)-//:4,(23 45)07*8 45)0*// 45)07*890165) 45)07*890)-//:4,(23 ?5@48.90A*.;4,.!%:.1665,16- results requires 15mm button
  45. !"" #"" $"" %"" &"" '!"" '#"" '$"" '%"" '&""

    ()*+,(,-.*/0(-1230,.41( 45)0165) 45)0)-//:4,(23 45)0165)90)-//:4,(23 45)07*8 45)0*// 45)07*890165) 45)07*890)-//:4,(23 ?5@48.90A*.;4,.!%:.1665,16- results requires 5.4mm button requires 15mm button
  46. !"" #"" $"" %"" &"" '!"" '#"" '$"" '%"" '&""

    ()*+,(,-.*/0(-1230,.41( 45)0165) 45)0)-//:4,(23 45)0165)90)-//:4,(23 45)07*8 45)0*// 45)07*890165) 45)07*890)-//:4,(23 ~three times more accurate ?5@48.90A*.;4,.!%:.1665,16- results requires 5.4mm button requires 15mm button
  47. !"" #"" $"" %"" &"" '!"" '#"" '$"" '%"" '&""

    ()*+,(,-.*/0(-1230,.41( 45)0165) 45)0)-//:4,(23 45)0165)90)-//:4,(23 45)07*8 45)0*// 45)07*890165) 45)07*890)-//:4,(23 ~three times more accurate allows 3x smaller devices ?5@48.90A*.;4,.!%:.1665,16- results requires 5.4mm button requires 15mm button
  48. target 1yaw 1cm (participant #4, roll varied)

  49. target 1yaw 1cm (participant #4, roll varied)

  50. 2pitch 90° 65° 45° 25° 15° 1cm (participant #10, tilt

    varied)
  51. (participant #1, roll varied) 3roll 1cm 15° 15° 0° 45

    90 -
  52. ! ! " " # # $ $ % %

    & & ' ' ( ( )* )* )) )) )! )! ) ) all data by participant #1-#12 tilt 4users 1cm roll
  53. ! ! " " # # $ $ % %

    & & ' ' ( ( )* )* )) )) )! )! ) ) all data by participant #1-#12 tilt 4users 1cm roll
  54. participant #3 and #4 4users !"# $%# &"# '"# ("#

    '"# $%# ("# !"# &"# 1cm
  55. !"" #"" $"" %"" &"" '!"" '#"" '$"" '%"" '&""

    ()*+,(,-.*/0(-1230,.41( 45)0165) 45)0)-//74,(23 45)0165)80)-//74,(23 45)09*: 45)0*// 45)09*:80165) 45)09*:80)-//74,(23 results ?5@48.90A*.;4,.!%:.1665,16- requires 15mm button requires 5.4mm button
  56. None
  57. how (in)accurate current devices are (button must be that big)

  58. if we knew the pad orientation

  59. if we knew nger angles

  60. None
  61. also need to know user ID, or we will overcompensate

    for people like this one
  62. !"" #"" $"" %"" &"" '!"" '#"" '$"" '%"" '&""

    ()*+,(,-.*/0(-1230,.41( 45)0165) 45)0)-//74,(23 45)0165)80)-//74,(23 45)09*: 45)0*// 45)09*:80165) 45)09*:80)-//74,(23 ?5@48.90A*.;4,.!%:.1665,16- requires 15mm button requires 5.4mm button results
  63. !"" #"" $"" %"" &"" '!"" '#"" '$"" '%"" '&""

    ()*+,(,-.*/0(-1230,.41( 45)0165) 45)0)-//74,(23 45)0165)80)-//74,(23 45)09*: 45)0*// 45)09*:80165) 45)09*:80)-//74,(23 ?5@48.90A*.;4,.!%:.1665,16- requires 15mm button requires 5.4mm button results fat nger problem compensation for respectively perceived input point
  64. !"" #"" $"" %"" &"" '!"" '#"" '$"" '%"" '&""

    ()*+,(,-.*/0(-1230,.41( 45)0165) 45)0)-//74,(23 45)0165)80)-//74,(23 45)09*: 45)0*// 45)09*:80165) 45)09*:80)-//74,(23 ?5@48.90A*.;4,.!%:.1665,16- requires 15mm button requires 5.4mm button shouldn’t we be able to make such a device? results fat nger problem compensation for respectively perceived input point
  65. Part 1 (science): the Generalized Perceived Input Point Model Part

    2 (engineering): sensing ngers in 3D ! highly accurate touch
  66. None
  67. optical tracker

  68. optical tracker what do you mean: “not very practical”? retro

    re ective markers on nger 8 camera setup makes a great “gold standard” implementation to test the concept
  69. mobile okay, maybe something a bit more

  70. None
  71. None
  72. None
  73. None
  74. devices that sense touch and ngerprints already exist

  75. gets everything a traditional touchpad gets + roll, pitch, yaw,

    & participant ID
  76. ! this is very di erent from MicroRolls [Roudaut et

    al. CHI’09]
  77. calibration have user touch a known target repeatedly and with

    di erent nger postures ! create database of ( ngerprint, target o set) use obtain ngerprint as user touches the device look up similar ngerprints in the database aggregate associated o sets (k nearest neighbor) and apply it algorithm
  78. user study 2

  79. 0 tracking device ngerprint optical tracker “simulated capacitive” (just contact

    area)
  80. 1 yaw

  81. 2 roll & ,422 ,422 '&%# "# &%# $%# !"#

    =0/63 &%# ! ! ! ! ! )%# ! $%# ! ! ! ! ! (%# ! !"# ! 3 pitch
  82. optical beats simulated capacitive by ~3x (based on user study

    1) ngerprint beats simulated capacitive (let’s nd out by how much) hypotheses
  83. 2 rotations × 13 angles × 5 repetitions per angle

    × 5 blocks = 650 trials / participant 12 participants design
  84. results

  85. 0 mm 2 mm 4 mm 6 mm 8 mm

    10 mm 12 mm 14 mm 16 mm contact area ngerprint scanner optical tracker results
  86. as expected a factor of 3x 0 mm 2 mm

    4 mm 6 mm 8 mm 10 mm 12 mm 14 mm 16 mm contact area ngerprint scanner optical tracker results
  87. as expected a factor of 3x factor 1.8 0 mm

    2 mm 4 mm 6 mm 8 mm 10 mm 12 mm 14 mm 16 mm contact area ngerprint scanner optical tracker results
  88. as expected a factor of 3x factor 1.8 potential 0

    mm 2 mm 4 mm 6 mm 8 mm 10 mm 12 mm 14 mm 16 mm contact area ngerprint scanner optical tracker results
  89. Part 1 (science): the Generalized Perceived Input Point Model Part

    2 (engineering): sensing ngers in 3D ! highly accurate touch
  90. science 2D model of touch is an oversimpli cation 2/3

    of touch inaccuracy is not the fat nger problem but a systematic e ect we call this: generalized perceived input point model
  91. engineering bene ts 1. make more reliable touch input devices

    2. use targeting aids (shift...) less often 3. make smaller mobile touch devices
  92. future work speed up learning make real-time interactive prototype miniaturize

    technology for mobile use
  93. human-computer interaction

  94. ! ! " " # # $ $ % %

    & & ' ' ( ( )* )* )) )) )! )! ) ) tilt 1cm roll Q?