Completion Time Predictions for Secondary Tasks in Non-Stationary Environments

1. Completion Time Predictions for Secondary Tasks in Non-Stationary Environments Sc.D. Dissertation Proposal Presentation Martin J. Schedlbauer April 28, 2005 Chair: Committee: Dr. Jesse M. Heines, University of Massachusetts Lowell Dr. Giampiero Pecelli, University of Massachusetts Lowell Dr. Holly Yanco, University of Massachusetts Lowell Dr. Robert Pastel, Michigan Technological University

2. Motivation • Mobile computing devices are becoming increasingly prevalent: • PDA • Mobile Phones • Personal GPS • Navigation systems: • In-Vehicle Information Systems • Marine Navigation Systems

3. The Problem • Current human-computer interface design is geared toward desktop computing. • Interaction patterns and task completion times are likely affected when the user interacts with an application in a non-stationary environment. • The interaction with the application is frequently a secondary task.

4. An Example Environment

5. Example: Navigation System • Maptech Navigator • 12” LCD display • Touch screen • Electronic charting • GPS navigation • Operational data

6. Task Completion Time • Task Completion Time (TT) is defined as the time it takes to perform a series of sub-tasks that constitute an interactive transaction with a system: • Completion time increases in dual-task situations. where LT is learning time, RT is the reaction (or recognition) time, MT is the mean movement time, and GT is glance time

7. HCI Engineering Models • Movement Time Predictions • Fitts’ Law and recent variations, including Meyer’s Law, Kvålseth’s Law, Oel’s Formulation • Reaction Time Predictions • Hick-Hyman Law • Kvålseth's Decision Model • Learning Time Predictions • Power Law of Practice • Heathcote’s Exponential Law of Practice

8. Fitts’ Law • Derived from Shannon-Hartley Theorem • Different formulations by Fitts, Welford, and MacKenzie determine value of ε • Describes univariate pointing tasks • Shown to apply to many different input devices where a and k are empirically derived coefficients, D is distance to target (amplitude of movement), and We is effective width of the target.

9. Movement along two dimensions Cannot simply use width of target Angle of approach has effect on T Target W' H D W φ Bivariate Pointing Tasks Accot-Zhai MacKenzie-Buxton

10. Alternative Models • Some alternative models provide more accurate predictions in many cases: • Meyer’s Law • Kvålseth's Law

11. Hick­Hyman Law • Model for predicting reaction time when making a decision among n choices: • Alternative model by Kvålseth: where k is empirically derived, pi is the probability of choice i.

12. Law of Practice • The time it takes to perform a task on the nth try can be stated as: • Alternative model by Heathcote et al.: where a is an empirically derived constant and T1 is the time of first try.

13. Applicable research concerning: small targets moving targets non-rectangular targets expanding targets area cursors goal crossing tasks steering tasks Touch input and probe width corrections Additionally, throughput varies with input device, lag and gain ISO9241-9 Relevant Research

14. Input Methods & Devices • Since the research will be focused on marine and automotive navigation systems, a survey of present systems in use resulted in the following findings: • Touch screens are employed more frequently • Trackball, isometric joystick, cursor rocker pads, and fixed buttons are most common • Variable footprint devices are not in use

15. Other Relevant Research • Besides Fitts’ Law, other related research is of importance: • In-Vehicle Navigation Systems • Dual-Task Situations • 15-Second Rule • Psychology • Task Scheduling Patterns • Kinesiology • Movement theories

16. Kinesthetic Models • Different theories of kinesiology: • iterative corrections model • series of discrete submovements • impulse variability model • initial muscle impulse then gliding • optimized initial impulse model • initial impulse, some corrective submovements • Models presume a closed-loop with visual and proprioceptive feedback.

17. Research Questions • Does Fitts’ Law hold in non-stationary environments? • Does movement time increase during secondary tasks? • How can task completion time be calculated? • How should user interfaces for mobile computing systems be designed?

18. Hypotheses • H10: For rapid aiming tasks in non-stationary environments, the correlation between MT and ID is as predicted by the General Fitts formulation. • H20: Posture does not have an effect on MT in a rapid aiming task when using either finger or stylus as a direct input probe.

19. Hypotheses (cont.) • H30: Performing rapid aiming tasks in a dual-task situation does not have an effect on MT. • H40: Acquisition of vibrating targets can be predicted by the General Fitts formulation.

20. Hypotheses (cont.) • H50: There is no difference in ID and error ratefor target acquisition using a trackball compared to using a finger/stylus touch in a non-stationary environment compared to a stationary one. • H6a: TT in non-stationary and stationary environments are positively correlated. • H7a: TT for secondary tasks in a non-stationary and stationary environments are positively correlated. • H8a: TT is proportional to MT + GT.

21. Experiments • The hypotheses will be investigated experimentally through a series of laboratory and field tests with human subjects. • The experiments will be carried out using a specially developed Java software platform.

22. Experiment I • Control experiment with general Fitts tasks: • Randomly placed square targets of different sizes • Acquisition with finger and stylus, trackball, and isometric joystick • Sitting and standing posture • 20-25 participants

23. Experiment II • Acquisition of vibrating targets that simulate motion in the environment: • Touch screen with finger and stylus only • Several variations of the frequency and amplitude of vibration • Variation of target distance and location, and possibly extent • Standing versus sitting posture • Same 20-25 participants

24. Experiment III • Performance of Fitts tasks is a dual-task situation: • Setup as in experiment I • Monitoring of a “depth indicator” and oral statement when threshold is reached • Measures glance time and effect of cognitive tasks on motor skills • 20-25 participants

25. Experiment IV • If time allows, subjects carry out Fitts tasks while walking: • Two speeds of walking • Randomly placed square targets of varying sizes • Touch input only with finger and stylus • 10-15 participants that are a subset of the subjects of experiment I

26. Experiment V • Fitts tasks and data entry in a non-stationary environment: • Single and dual-task situations • Carried out on an actual boat • Accelerometer and pendulum apparatus used to quantify motion • 8-12 participants

27. Summary of Research Goals • Investigation of the applicability of Fitts’ Law and other movement time prediction models to • non-stationary environments with standing posture • secondary tasks in dual-task situations • Development of an empirically derived predictive model for completion time of secondary tasks in non-stationary environments. • Heuristics for user interface design of mobile computing applications with a specific focus on marine navigation systems.

28. Dissertation Schedule

29. Questions & Answers ? • Copies of the proposal are available for those interested. • Research results will be posted at: • Comments and feedback are welcome. www.cs.uml.edu/~mschedlb

30. SCINS Survey

31. Rocker-type cursor pad that moves cursor up, down, left, and right Numeric keypad for waypoint entry Soft buttons whose purpose depends on context Typical SCINS

32. Usability Model

33. Moving Targets Jagacinski et al. Hoffmann

34. Dual-Task Situations • In situations, where users perform two tasks (primary and secondary), completion time is affected: • Learning time and reaction time can be made constant.