Presentation of our full paper at the CHI conference on Human Factors in Computing Systems, San Jose, 2016. Abstract: This paper revisits the present understanding of typing, which originates mostly from studies of trained typists using the ten- finger touch typing system. Our goal is to characterise the majority of present-day users who are untrained and employ diverse, self-taught techniques. In a transcription task, we compare self-taught typists and those that took a touch typing course. We report several differences in performance, gaze deployment and movement strategies. The most surprising finding is that self-taught typists can achieve performance levels comparable with touch typists, even when using fewer fingers. Motion capture data exposes 3 predictors of high performance: 1) unambiguous mapping (a letter is consistently pressed by the same finger), 2) active preparation of upcoming keystrokes, and 3) minimal global hand motion. We release an extensive dataset on everyday typing behavior. More information about the work: http://userinterfaces.aalto.fi/how-we-type/

1. How we type
Movement Strategies and Performance in Everyday Typing
Anna Maria Feit, Daryl Weir, Antti Oulasvirta
Aalto University, Helsinki, Finland

2. What do you know about how you type?

3. Touch Typist

4. flickr.com/photos/
aemays/15818888836
1959

5. • Fixed finger-to-key
Mapping
• Eyes free
• Hands static

6. Touch Typist ?

7. • Fixed finger-to-key
Mapping
• Eyes free
• Hands static
What about
non-touch typists?
?

8. Professional typists from 1920s – 1980s:
• Avg. speed: 75 wpm
[e.g. Grudin, 1983]
• Random letters typed less than half as
fast
[e.g. Shaffer, 1968]
• Hand alternation: 30-60 ms faster
[e.g. Salthouse, 1984]
What we do knowWhat we do know

9. Basis for Models and Theories
• Central control model [Terzuolo and Viviana, 1980]
• Mathematical model [Hiraga, 1980]
• Simulation model [Rumelhart and Norman,1982]
• Heuristic model [Salthouse, 1986]
• TYPIST model [John, 1996]
• Queuing Network Model [Wu and Liu, 2008]

11. RsultsStudy of Everyday Typists

12. Motion capture
Reference video
Typing data
Eye tracking

13. 30 Typists (17 female)
Age: 20 – 55
Performance: 34 -79 wpm
Language:
English or Finnish

14. 13 learned the touch
typing system
“Touch typists”
17 never took a typing
course
“Non-touch typists”

15. Sentences
Voitko jo paremmin? Are you feeling better?
Random
vrphme vrphme
Mix
Voitko jo vrphme paremmin? Are you vrphme feeling better?
Task and Stimulus

16. Motion Capture data:
x-y-z coordinates of 52
markers at 240 fps
The HOW-WE-TYPE Dataset

17. Motion Capture data:
x-y-z coordinates of 52
markers at 240 fps
Keypress data:
executing finger,
inter-key interval, etc.
The HOW-WE-TYPE Dataset

18. Motion Capture data:
x-y-z coordinates of 52
markers at 240 fps
Keypress data:
executing finger,
inter-key interval, etc.
Eye tracking:
Gaze annotation
The HOW-WE-TYPE Dataset

19. Motion Capture data:
x-y-z coordinates of 52
markers at 240 fps
Keypress data:
executing finger,
inter-key interval, etc.
Eye tracking:
Gaze annotation
Reference video
The HOW-WE-TYPE Dataset
Published!

20. Rsults
→ Paper
Results

21. No difference in
• Performance
• Errors (uncorrected)
• Efficiency

22. So, what makes a typist fast?So, what makes a typist fast?

23. 1. Entropy
2. Preparation
3. Global movement
+ Visual attention

24. Fast typists
Avg
Average entropy of which
finger presses which key
use their fingers consistently

25. Average distance of finger
to key at preceding key press
Fast typists prepare key presses

26. Average SD of location
of the hand markers
Fast typists do not move their hands

27. percentage of time looking at fingers
Fast typists do not look at their hands

28. For touch or non-
touch typists
• Fixed finger-to-key
Mapping
• Eyes free
• Hands static
• Prepare keypresses
Fast typists

29. • Fixed finger-to-key
Mapping
• Eyes free
• Hands static
• Prepare keypresses
?
Fast typists

30. Touch Typists:
8.54 (SD 1.08)
Non-touch Typists:
6.24 (SD 0.94)
Fast typists use any number of fingers

33. Typing strategies
→ Paper

34. • Fixed finger-to-key
Mapping
• Eyes free
• Hands static
• Prepare keypresses
Fast typists

35. userinterfaces.aalto.fi/how-we-type
30 typists
4500 phrases
Motion capture – Eye gaze – Keypresses