Interactive Environments context and task theory interaction techniques in/output technologies
Environments Christmas lectures context and task theory interaction techniques • 17.12. 10-12h MMI2: guest lecture by Christian Holz http://www.christianholz.net in/output technologies • 16.12. 10-12h, B101, Infoviz: christmas lecture with optical illusions and visual fun – bring cookies – material won‘t be asked in the exam 2 LMU München — Medieninformatik — Andreas Butz, Julie Wagner — � HCI II — WS2014/15 Slide
Environments Some Theory for Instrumented Env. context and task • Pointing (...again???, really??? ;-) theory – yes, because we finally move to 3D! pointing interaction • Crowd Sourcing (huh?!?) techniques – yes, because instr. env. are inhabited by people in/output technologies • Spatial Augmented Reality (what???) – yes, because that looks like the perfect mixture of virtual and physical worlds... 3 LMU München — Medieninformatik — Andreas Butz, Julie Wagner — � HCI II — WS2014/15 Slide
Environments pointing in mid-air context and task • pointing in desktop or mobile environments – models in which users either touch a target directly theory or translates an input device to cause a proportional pointing translation of a cursor • Distal pointing makes use of different types of interaction techniques movement (e.g. wrist rotation.) – both position and orientation of input device in/output technologies determines the cursor position. Literature: Kopper R. et al.: A human motor behavior model for distal pointing tasks, International Journal of Human-Computer Studies, Volume 68 Issue 10 (2010) 4 LMU München — Medieninformatik — Andreas Butz, Julie Wagner — � HCI II — WS2014/15 Slide
Environments RayCasting context and task • place cursor at point where ray emanating from the index finger intersects the screen. theory – problems: jittery cursor movements due to natural interaction hand tremors. techniques – solution: in/output • use of hand palm or forearm technologies – reduces some of jittery with body-parts more proximal in the kinematic chain. • use filtering techniques – e.g. Kalman filter, two stage mean filter based on angular velocity, etc. Literature: Vogel, D.: Distant Freehand Pointing and Clicking on Very Large, High Resolution Displays 5 LMU München — Medieninformatik — Andreas Butz, Julie Wagner — � HCI II — WS2014/15 Slide
Environments Repetition context and task • human motor behavior model for pointing tasks? theory – Fitts’ law pointing – time to acquire a target is dependent on its size and on the amplitude of movement. interaction techniques • MT = a+b * ID • a,b, empirically determined constants in/output technologies • ID = index of difficulty of the task Target 1 Target 2 � � A ID ¼ log 2 W þ 1 ; Why do you think distal pointing is not well described using Fitts’ law. What might be other factors that influence the pointing time? Literature: Kopper R. et al.: A human motor behavior model for distal pointing tasks, International Journal of Human-Computer Studies, Volume 68 Issue 10 (2010) 6 LMU München — Medieninformatik — Andreas Butz, Julie Wagner — � HCI II — WS2014/15 Slide
Environments Integrating D into Fitts’ ID context and task • reason for W 2 – decrease in performance as W gets D3 D1 D2 theory D4 smaller is approximately proportional to pointing • decrease in performance as A gets larger interaction • decrease in performance as D gets � � A � D techniques ID RAW ¼ log 2 W 2 þ 1 larger : • accounts for the users distance to in/output technologies the display (D) – problem: unclear which value should be used for D if distance to initial pointing location different from distance to final pointing location. – solution: resolve ambiguity by using angular measurements of target size and movement amplitude Literature: Kopper R. et al.: A human motor behavior model for distal pointing tasks, International Journal of Human-Computer Studies, Volume 68 Issue 10 (2010) 7 LMU München — Medieninformatik — Andreas Butz, Julie Wagner — � HCI II — WS2014/15 Slide
Integrating angular measurements Environments for ID context and task • the amplitude of user movement in a distal theory pointing task decreases as user moves away pointing from display (arm/wrist rotation is smaller) – more appropriate parameters: interaction techniques • angular movement ( α ) A w • angular size of target ( ω ) in/output D technologies ω α � � a ¼ 2arctan 0 : 5 A ; D � � � � o ¼ arctan 0 : 5 ð A þ W Þ � arctan 0 : 5 ð A � W Þ ; D D � a � ID ANGULAR ¼ log 2 o k þ 1 : Literature: Kopper R. et al.: A human motor behavior model for distal pointing tasks, International Journal of Human-Computer Studies, Volume 68 Issue 10 (2010) 8 LMU München — Medieninformatik — Andreas Butz, Julie Wagner — � HCI II — WS2014/15 Slide
Environments context and task � a � ID ANGULAR ¼ log 2 o k þ 1 theory : pointing • k is a constant power factor determining the interaction relative weights of ω and α . techniques – not always a linear relationship in/output • pointing consists of two phases: technologies –ballistic phase: pointer moves very rapidly to point –correction phase: fine-grained adjustments to acquire target. • natural hand tremor • Heisenberg effect: unintentional movement of cursor when a button is pressed Literature: Kopper R. et al.: A human motor behavior model for distal pointing tasks, International Journal of Human-Computer Studies, Volume 68 Issue 10 (2010) 9 LMU München — Medieninformatik — Andreas Butz, Julie Wagner — � HCI II — WS2014/15 Slide
Environments Testing various possibilities for ID context and task • Regression analysis ID vs. ID raw vs. ID angular : – find the best model of human motor behavior theory • ID: R 2 = 0.686 pointing – 30% of data points cannot be explained by the interaction model. techniques – take the users’ distance to display into account! in/output Table 1 technologies Fit of Fitts’ law for each distance to the display. 4.5 R 2 D ( m ) a b RMS 4 3.5 1 � 0.204 0.402 0.106 0.963 2 � 0.362 0.502 0.267 0.864 3 3 � 0.707 0.672 0.484 0.776 MT (s) 2.5 2 1.5 D 1 1 2 0.5 3 0 2 2.5 3 3.5 4 4.5 5 5.5 6 ID Literature: Kopper R. et al.: A human motor behavior model for distal pointing tasks, International Journal of Human-Computer Studies, Volume 68 Issue 10 (2010) 10 LMU München — Medieninformatik — Andreas Butz, Julie Wagner — � HCI II — WS2014/15 Slide
Environments Testing various possibilities for ID context and task • Regression analysis ID vs. ID raw vs. ID angular : – find the best model of human motor behavior theory • ID raw : R 2 = 0.928 pointing • users stood in the center of movement interaction – less generic model techniques – in the experimental setup people stood in the center in/output of movement. technologies D3 D1 D2 D4 � � A � D ID RAW ¼ log 2 W 2 þ 1 : Literature: Kopper R. et al.: A human motor behavior model for distal pointing tasks, International Journal of Human-Computer Studies, Volume 68 Issue 10 (2010) 11 LMU München — Medieninformatik — Andreas Butz, Julie Wagner — � HCI II — WS2014/15 Slide
Environments Testing various possibilities for ID context and task • Regression analysis ID vs. ID raw vs. ID angular : – find the best model of human motor behavior theory • ID angular : R 2 = 0.929 (k=3) pointing • more generic and expressive interaction techniques • outliers for high index of difficulty – as angular width gets extremely small, a linear in/output technologies increase in acquisition time is not adequate • hand tremor and Heisenberg effect A w D 4.5 ω 4 α 3.5 3 MT (s) 2.5 2 1.5 1 � a � ID ANGULAR ¼ log 2 o k þ 1 0.5 : 0 0 1 2 3 4 5 6 7 8 9 ID ANGULAR Literature: Kopper R. et al.: A human motor behavior model for distal pointing tasks, International Journal of Human-Computer Studies, Volume 68 Issue 10 (2010) 12 LMU München — Medieninformatik — Andreas Butz, Julie Wagner — � HCI II — WS2014/15 Slide
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