Touch Input. CSE 510 Christian Holz Microsoft Research February 11, 2016

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1 Touch Input CSE 510 Christian Holz Microsoft Research February 11, 2016

2 hall of fame/hall of shame? Nokia 5800, 2008

3 hall of fame/hall of shame? stylus

4

5 we ve invented

6 [Lightpen 62]

7 [E.A. Johnson 65]

8 [Plato IV '72]

9

10

11 IBM Simon, 1992

12 what was the real novelty here? 30 second brainstorming

13 1 2 Touch technologies Touch accuracy

14 Touch technologies a cursory overview

15 resistive sensing

16 Inspiron 7000,1998

17 Compaq PDA, 2000

18 [Matsushita et al., UIST 00]

19 how did they enable dual-touch in a single touch sensor? 30 second brainstorming [Matsushita et al., UIST 00]

20 resistive sensing

21 capacitive sensing

22 capacitive sensing

23 DiamondTouch [Dietz & Leigh '01]

24 DiamondTouch [Dietz & Leigh '01]

25 SmartSkin [Rekimoto '02]

26 SmartSkin [Rekimoto '02]

27 Fingerworks, 2005

28 iphone 1, 2007

29 and it prevailed

30 and it prevailed

31 Bodyprint [CHI '15]

32 Biometric Touch Sensing [UIST '15]

33 optical touch sensing

34 walls & tables

35 camera invisible (infrared) illumination visible projector imaging surface optical touch sensing

36 diffuse illumination

37 diffuser camera illuminant

38

39 space to diffuse

40 darker and blurry

41 projection plane

42 projection plane rear DI

43 HoloWall [Rekimoto, UIST '97]

44 Microsoft Surface, 2008

45

46

47 frustrated total internal reflection the other camp

48

49 projection plane FTIR [Han, UIST 05]

50 fingerprint scanners camera light source

51 Total Internal Reflection Acrylic Pane LED Scattered Light Baffle Diffuser Projector Video Camera FTIR [Han, UIST 05]

52 [Han, UIST 05]

53 ( weird mixes diffuse illumination + frustrated Fresnel reflection

54 [Fiberio, UIST '15]

55

56

57 diffuse reflection + surface reflection diffuse reflection + surface reflection diffuse reflection frustrated surface reflection

58 )

59 what s the big difference? 30 second brainstorming diffuse illumination FTIR

60 diffuse illumination FTIR

61 .5 Touch processing

62 ??? typical processing pipeline

63 segment objects find connected components track components typical processing pipeline

64 segment objects find connected components track components who sees the link to Buxton s Touch, Gesture & Marking? 30 second brainstorming typical processing pipeline

65 this part also calculates accurate input locations segment objects find connected components track components typical processing pipeline

66 Touch accuracy

67 Biometric Touch Sensing [UIST '15]

68 input resolution: 42 x 33 across a 10 display map to an accurate input location screen resolution: 2,160 x 1,440

69 center of gravity

70 if only it were that easy :-)

71 Information Kiosks [Plaisant et al. '88] Home Automation [Plaisant et al. '90 and on] Touch painting [Sears et al. '91] Home Automation [Plaisant et al. '90 and on]

72 sources of error?

73 parallax between the touch screen surface and the display surface high error shown in many studies fatigue in arm motion [Potter et al. CHI '88]

74 parallax between the touch screen surface and the display surface high error shown in many studies fatigue in arm motion solution: finger mouse, a cursor the user drags on the screen [Potter et al. CHI '88]

75 AK HI ME NJ!;D AL IA MI NM TN AR ID MN NV TX AZ IL MO NY UT CA IN MS OH VA CO KS MT OK VT CT KY NC OR WA DE LA ND PA WI FL MA NE RI WV GA MD NH SC WY [Potter et al. CHI '88]

76 parallax between the touch screen surface and the display surface high error shown in many studies fatigue in arm motion solution: finger mouse, a cursor the user drags on the screen strategies: touch-down, first-contact, lift-off + offset cursor [Potter et al. CHI '88]

77 okay, let s use cursors then

78 High precision touch screen interaction [Albinsson and Zhai, CHI '03]

79 High precision touch screen interaction [Albinsson and Zhai, CHI '03]

80 High precision touch screen interaction [Albinsson and Zhai, CHI '03]

81 High precision touch screen interaction [Albinsson and Zhai, CHI '03]

82 Precise Selection Techniques [Benko et al., CHI '06]

83 Precise Selection Techniques [Benko et al., CHI '06]

84 Precise Selection Techniques [Benko et al., CHI '06]

85 vertical finger pitch contact area flat finger pitch Direct-touch vs. mouse input [Forlines et al., CHI '07]

86 the culprit: the fat-finger problem

87 fat finger

88 fat finger

89 LucidTouch [Wigdor et al., CHI '07]

90 [Baudisch and Chu, CHI '09]

91 Shift [Vogel and Baudisch, CHI '07]

92 why did you read this paper? 30 second brainstorming Shift [Vogel and Baudisch, CHI '07]

93 perceived input point problem [Vogel and Baudisch, CHI '07]

94 showing cursors is cheating! and they almost convinced us!

95

96 what s the real problem here?

97 the problem is underspecified!

98 which link am I selecting?

99 could it be that it is not the fingers but our touch devices that are wrong?

100 let s assume for a second that there is no fat finger problem

101 instead, almost all observed targeting error comes from perceived input point problem

102 perceived input point problem touch device perceives target

103 why we hope it s the perceived input point problem? offset we can correct for it the fat finger problem, in contrast is always noise = error

104 our main hypothesis while there is always an offset, we hypothesize that the offset depends on the pointing situation

105 1 yaw

106 2 pitch

107 3 roll

108 4 users: finger shape

109 4 users: mental model target target

110 current model x/y x/y center of contact area 2D screen

111 we propose 3D x/y sensing the finger in 3D 2D screen

112 user study

113 independent variables yaw pitch roll

114 error metric

115 error metric spread := variation within a condition

116 error metric spread := variation within a condition minimum button size := 95% of samples across conditions

117 study design 2 yaw 2 sessions (pitch, roll) 5 angles 6 repetitions per angle 5 blocks = 600 trials / participant 12 participants

118 results

119 pitch target cm

120 roll target cm

121 user #1 #2 #3 #4 pitch 1cm

122 user #1 #2 #3 #4 which user is the most accurate? 30 second pitch brainstorming 1cm

123 minimum button size 18mm 16mm 14mm mm 10mm 8mm mm 5.4 4mm 2mm 0mm

124 minimum button size 18mm 16mm 14mm 12mm 10mm 8mm 6mm 4mm 2mm requires 15mm button times higher accuracy requires 5.4mm button mm

125 can we make this real?

126 Ridgepad optical fingerprint scanner 500 dpi pixels

127 touchpad vs. fingerprint scanner 2D contact area 2D contact area + yaw, pitch, roll + participant ID

128 user study

129 minimum button size 14mm 12mm 10mm 8mm 6mm 4mm 1.8x higher accuracy 2mm 0mm contact area Ridgepad optical tracker

130 minimum button size 14mm 12mm 10mm 8mm 6mm 4mm 2mm 3.3x higher accuracy 0mm contact area Ridgepad optical tracker

131 now we re done and touch is accurate.

132 no! there s a bug here! we re still compensating

133 systematic effect

134 3D device 2D

135 3D user user -1 2D

136 3D user? 2D

137 goal: trying to understand

138 challenge

139 challenge we need a model in HCI, models are typically obtained using an unambiguous device (e.g., mouse) 1. measure data points 2. fit a curve (34, 119)

140 but what shall we measure? there are infinite ways how users might map these crosshairs to 3D

141 so we had to revert to basic experimental process...

142 guess a model try it out in an experiment if outcome is bad, repeat

143 which model? if it is not the contact area...

144 creating models using visual features

145

146 7x7

147

148 evaluating the models bad model large error offsets good model small error offsets

149 3 user studies

150

151 independent variables roll x x x x x x x x x x x x pitch

152 ...and head position

153 study design 6 combinations of finger angles (pitch, roll) 4 head positions 2 blocks 4 repetitions = 192 trials / participant participants

154 results

155

156 contact area model

157

158 projected center model

159 projected center model

160

161 minimum button size 14mm 14.6mm 12mm 10mm 8mm 6mm 4mm 2mm 0mm 3x higher accuracy 4.8mm contact area projected center

162 main insight touch input is a 3D operation

163 users target using features on top of the finger parallax current devices sense features at the bottom of finger

164 now we have two options

165 1) We implement users mental models Imaginary Phone [UIST '11]

166 2) We compensate for errors Ridgepad reconstructs the finger in 3D input-only not real-time

167 [Fiberio, UIST '15]

168 1 2 Touch technologies Touch accuracy

169 Touch Input CSE 510 Christian Holz Microsoft Research February 11, 2016

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