Download presentation
Presentation is loading. Please wait.
Published byJames Fowler Modified over 9 years ago
1
===!"§ Deutsche Telekom Laboratories Target Acquisition with Camera Phones when used as Magic Lenses CHI 2008, Florence, Italy, April 9, 2008 Michael Rohs Deutsche Telekom Laboratories Technische Universität Berlin Antti Oulasvirta Helsinki Institute for Information Technology HIIT
2
Page 2 ===!"§ Deutsche Telekom Laboratories See-Through Interfaces for Camera Phones Augmented reality (AR) applications project information onto real world scenery in real time — Camera phone shows this layer through narrow viewport Navigation by moving the device — Spatially-aware displays [Fitzmaurice] — Peephole displays [Yee]
3
Page 3 ===!"§ Deutsche Telekom Laboratories Magic Lens for Paper Maps
4
Page 4 ===!"§ Deutsche Telekom Laboratories Magic Lens for AR Widgets Printable user interface elements — Embedded in user’s environment — Entry point for interaction — Controlled via position, rotation, distance Background layer — Passive widget Overlay layer — Active Display — Camera phone as “see-through tool”
5
Page 5 ===!"§ Deutsche Telekom Laboratories Dynamic Peephole Pointing & Magic Lens Pointing Dynamic peephole pointing Movement of peephole corresponds to movement through virtual space No visual context beyond display Targets dynamically revealed Magic lens pointing Camera phone tracks its position above a meaningful background Visual context beyond display Targets always visible
6
Page 6 ===!"§ Deutsche Telekom Laboratories Target Acquisition with Camera Phones Two distinct categories of interfaces — Dynamic peephole: Objects of interest only in virtual — Magic lens: Objects of interest on the physical surface Analyzing target acquisition with camera phones — Performance — Modeling
7
Page 7 ===!"§ Deutsche Telekom Laboratories Overview Analysis of the Pointing Tasks Two-Component Model of Magic Lens Pointing Experiment 1: Dynamic Peephole Pointing Experiment 2: Magic Lens Pointing
8
Page 8 ===!"§ Deutsche Telekom Laboratories Target Acquisition with Camera Phones View selection Screen distance range System delay Maximum movement velocity Display update rate Device movement in 3D space Gaze deployment between display and background
9
Page 9 ===!"§ Deutsche Telekom Laboratories Analysis of Dynamic Peephole Pointing Task Task: Move cursor onto target Target might not be visible in the peephole initially Visual feedback uniformly mediated by the device Hypothesis: Dynamic peephole pointing can be modeled by Fitts’ law MT = a o + b o ID with ID = log 2 (D / W + 1) Lag and low frame rates increase the coefficients a o and b o compared to direct observation of the target W D
10
Page 10 ===!"§ Deutsche Telekom Laboratories Analysis of Magic Lens Pointing Task Task: Move cursor onto target First phase: Target directly visible First task: Move lens over target Second phase: Target behind display Second task: Move crosshair over target T
11
Page 11 ===!"§ Deutsche Telekom Laboratories Analysis of Magic Lens Pointing Task Task: Move cursor onto target First phase: Target directly visible MT p = a p + b p log 2 (D / S + 1) Second phase: Target behind display MT v = a v + b v log 2 (S/2 / W + 1) S W W S/2 D T S D
12
Page 12 ===!"§ Deutsche Telekom Laboratories Magic Lens Pointing Model First phase (physical pointing): MT p = a p + b p log 2 (D / S + 1) Second phase (virtual pointing):MT v = a v + b v log 2 (S/2 / W + 1) n Two-component Fitts’ law model MT= MT p + MT v = a + b log 2 (D / S + 1) + c log 2 (S/2 / W + 1) S W D
13
Page 13 ===!"§ Deutsche Telekom Laboratories physical pointing Control Loops in Physical Pointing Iterative corrections model [Crossman & Goodeve] — Derive Fitts’ law from number of iterations through feedback loop — Movements >200 ms controlled by visual feedback — Ballistic submovements of constant time (135-290 ms) — Each submovement has distance error (4-7%) observe target distance P = 100 ms plan hand movement C = 70 ms perform hand movement M = 70 ms expected distance error
14
Page 14 ===!"§ Deutsche Telekom Laboratories Control Loops in Virtual Pointing Virtual pointing introduces delay in feedback loop Machine lag and frame rate [Ware & Balakrishnan] Time for submovement t = P + C + M and machine lag L Rewrite two-part model as MT = a + β t log 2 (D / S + 1) + γ (t + L ) log 2 (S/2 / W + 1) observe target distance P = 100 ms plan hand movement perform hand movement expected distance error C = 70 ms M = 70 ms virtual pointing compute device position L = 100–300 ms
15
Page 15 ===!"§ Deutsche Telekom Laboratories Dynamic Peephole Pointing Experiment Targets only visible on device display Device tracked on surface of size A0 Cyclical multi-direction target acquisition (ISO 9241-9) 9 targets on a circle, next one highlighted Nokia N80 for tracking and feedback (beeps, highlighting) 12 participants (10 male, 2 female, age 22-33) Task: Move crosshair over target and press joystick button Instructed to calibrate z-distance and explore space before start W = 13-97 mm, D = 55-535 mm, 33 combinations
16
Page 16 ===!"§ Deutsche Telekom Laboratories Results of Dynamic Peephole Pointing 12 subjects x 33 conditions x 3 rounds x 9 selections Mean movement time: 2.13 sec Mean error rate: 5%
17
Page 17 ===!"§ Deutsche Telekom Laboratories Fitts’ Law Model of Dynamic Peephole Pointing Basic Fitts’ law leads to accurate predictions MT= a + b log 2 (D / W + 1) = -636 + 881 log 2 (D / W + 1), R 2 = 0.93
18
Page 18 ===!"§ Deutsche Telekom Laboratories Magic Lens Pointing Experiment Targets visible on background and through lens Device tracked on plasma display of size A0 Cyclical multi-direction target acquisition (ISO 9241-9) 9 targets on a circle, next one highlighted Nokia 6630 for tracking and feedback (beeps, highlighting) 12 participants (8 male, 4 female, age 19-31) Task: Move crosshair over target and press joystick button Instructed to calibrate z-distance before start W = 13-97 mm, D = 55-535 mm, 33 combinations
19
Page 19 ===!"§ Deutsche Telekom Laboratories Results of Magic Lens Pointing 12 subjects x 33 conditions x 3 rounds x 9 selections Mean movement time: 1.22 sec Mean error rate: 7%
20
Page 20 ===!"§ Deutsche Telekom Laboratories Fitts’ Law Model of Magic Lens Pointing Fitts’ law does not accurately predict performance MT = 581 + 191 log 2 (D / W + 1), R 2 = 0.57
21
Page 21 ===!"§ Deutsche Telekom Laboratories Two-Part Fitts’ Law Model of Magic Lens Pointing Two-component Fitts’ law leads to better prediction Screen diagonal S = 2.8 cm n MT= a + b log 2 (D / S + 1) + c log 2 (S/2 / W + 1) = 383 + 112 log 2 (D / S + 1) + 811 log 2 (S/2 / W + 1), R 2 = 0.88
22
Page 22 ===!"§ Deutsche Telekom Laboratories Two-Part Fitts’ Law Model with Parameters a, b, c, S Treating lens size S as an additional parameter n MT = 384 + 113 log 2 (D / 2.99 + 1) + 776 log 2 (2.99/2 / W + 1), R 2 = 0.88 n Screen diagonal predicted to 2.99 cm (real value 2.8 cm) n Supports the validity of the model
23
Page 23 ===!"§ Deutsche Telekom Laboratories Conclusion Analyzed target acquisition with camera phones as — Dynamic peephole displays — Magic lenses Dynamic peephole pointing can be modeled with Fitts’ law — Interaction uniformly mediated by the device Magic lens pointing not adequately explainable by Fitts’ law — Initial physical pointing phase — Second virtual pointing phase n Developed two-part Fitts’ law model for magic lens pointing — MT = a + b log 2 (D / S + 1) + c log 2 (S/2 / W + 1) Numerous application ideas for magic lens interfaces
Similar presentations
© 2024 SlidePlayer.com. Inc.
All rights reserved.