Mnemonical Body Shortcuts: improving mobile interaction


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Motivation – To study and validate a body space based approach to improve mobile device interaction and on the move interaction performance.
Research approach – We developed and user evaluated (20 + 10 users) an adaptive inertial sensing based system featuring default and personalized body space gesture recognition with suitable feedback.
Findings/Design – Results present gestures as suitable shortcut for on the move action triggering, improving mobile interaction performance.
Research limitations/Implications – The evaluations were performed in a controlled scenario. Further studies should be performed in more demanding situations (public transportations, stairs).
Originality/Value – The research makes a contribution on the validation of body-space gestures to improve on the move interaction performance.
Take away message – Mnemonical Body Shortcuts improves shortcut triggering both in still and on the move scenarios.

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  • Mnemonical Body Shortcuts: improving mobile interaction

    1. 1. Mnemonical Body Shortcuts improving mobile interaction
    2. 2. Tiago Guerreiro [email_address] TU Lisbon Technical Superior Institute INESC-ID VIMMI Ricardo Gamboa Joaquim Jorge
    3. 3.
    4. 4. you're stuck in a moment And you can't get out of it - Bono ‘‘ ’’
    5. 5. Limited Screen Size
    6. 6. Multi-task Graphical Displays
    7. 7. Slow and Visually Demanding
    8. 8. Mother Father Messages Key Shortcuts Hard to memorize Girlfriend Agenda Calculator Dog
    9. 9. Can it be more natural?
    10. 10. Recognition Issues
    11. 11. Low Acceptance
    12. 12. Task Analysis Mobile Interaction and Shortcuts
    13. 13. <ul><li>20 Users </li></ul><ul><li>Mobile </li></ul><ul><li>Interaction </li></ul><ul><li>Habits </li></ul><ul><li>Shortcut </li></ul><ul><li>usage </li></ul><ul><li>observation </li></ul>
    14. 14. Calls, SMS, Agenda, Contact List and Watch 75% use Key shortcuts but do not recall most of the shortcuts Voice Shortcuts are not used ( unavailable or unconfident on recognition)
    15. 15. 4 clicks to access the most used applications 5 clicks to call the most used contacts .... although they were on fast dial
    16. 19. Body Mnemonics Ängeslevä et al.
    17. 21. Camera (Rohs et al., 2004) EMG (Costanza et al., 2005) Inertial Sensing (Choi et al., 2005) RFID ( Headon et al., 2003)
    18. 24. 100%
    19. 28. 20 Users Associate applications with keys and body parts
    20. 36. 20 Users 12 Gestures repeated 5 times Offline Recognition Analysis Knn suitable for default gestures Naive Bayes suitable to used-defined gestures
    21. 44.   Rate Errors without Multichoice or Cancelling 17,95% Errors using only Cancelling 14,14% Errors using only Multichoice 10,8% Errors 3,3%   Rate Clicks Average 2,5 clicks Time Average 3,8 seconds Stops during Shortcuts 1,4%
    22. 46. <ul><li>Evaluate the system in adverse situations </li></ul><ul><li>Adapt and evaluate it with blind users </li></ul><ul><li>Create a language model to totally rely on gestures to control the device </li></ul>
    23. 47. rbut
    24. 48. Mnemonical Body Shortcuts improving mobile interaction Tiago Guerreiro [email_address] t