[bone shaker] :: DIY health final

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  • Measurement, psychology, urban/city
  • “ layer of the poetic” to the system (make it human) How can this data be most effectively represented?
  • “ layer of the poetic” to the system (make it human) How can this data be most effectively represented?
  • “ layer of the poetic” to the system (make it human) How can this data be most effectively represented?
  • “ layer of the poetic” to the system (make it human) How can this data be most effectively represented?
  • “ layer of the poetic” to the system (make it human) How can this data be most effectively represented?
  • “ layer of the poetic” to the system (make it human) How can this data be most effectively represented?
  • “ layer of the poetic” to the system (make it human) How can this data be most effectively represented?
  • “ layer of the poetic” to the system (make it human) How can this data be most effectively represented?
  • MAKE THIS SIMPLER. Space and emotion… Affect of the city on the psyche… Serendipity 
  • MAKE THIS SIMPLER. Space and emotion… Affect of the city on the psyche… Serendipity 
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  • [bone shaker] :: DIY health final

    1. 1. Kimi Spencer [email_address] Bone Shaker DIY Health, Final Proposal | 14 November 2011
    2. 2. “ A state of complete physical , mental , and social well-being and not merely the absence of disease or infirmity .” World Health Organization
    3. 3. Health for Me Excitement, delight, and adventure. Absence of boredom; stimulation. Discovering meaningful connections. Joie de vivre .
    4. 4. The Situationist Lived experience over the spectacle . Derive (“t0 drift”) the spontaneous exploration of urban landscapes guided by aesthetic instinct .
    5. 5. Biking and the City Biking is an urban phenomena. Specific to cities with a vibrant cityscape and infrastructural support . A tool for expanded exploration .
    6. 6. Biking and Health “ A Copenhagen study of over 30,000 people ranging in age from 20 to 93 took place over 14.5 years and found that bike commuting an average of 3 hours per week decreased risk of mortality by about 40% over the control group that did not bike” Andersen, 2000
    7. 7. Smallest Target Behavior Bike for 30 minutes per day .
    8. 8. The User Urban city dweller. 20 to 30 years old. Educated , and technologically comfortable . Moderately well-off ( can afford a bike and smart phone ). Not an athlete .
    9. 9. The User’s Ability Safety issues. Bike paths. Places to lock bikes. Time . Routine . Effort .
    10. 10. Spokes NYC
    11. 11. Ride the City
    12. 12. The User’s Motivations Social acceptance /rejection. Biking is cool. Pain /Pleasure. Easily bored by repetition and lazy. Hope /Fear. Desire to get physically fit.
    13. 13. The User’s Motivations How to bring pleasure? It’s not about exercise, it’s about exploration . How to catalyze hope? Feedback on user’s progression .
    14. 14. Bikes as a Catalyst for Adventure
    15. 15. Classify the Behavior
    16. 16. The Environment Urban landscape, Sound data Temperature data Networks data … Layers of information
    17. 17. Hertzian Space The invisible frequencies that are “pervasive, and though invisible, in some way are present in every aspect of our physical experience”. Both real and imaginary . “ The dreams of electronic objects.”
    18. 18. Immaterials
    19. 19. Wiggle
    20. 20. WarBiking
    21. 21. Second Order Goal Experience the layers of the City though exploration .
    22. 22. Proposal An application that promotes urban biking by enabling users, and the exploration of the urban landscape in a new, delightful way.
    23. 23. Cybernetic Model
    24. 24. Network Diagram
    25. 25. Triggers Reminders after inactivity. Suggestions of places, events, layers, and times to bike. Notifications on friends’ urban adventures and biking activity.
    26. 26. Mobile, Real-Time Feedback Personal health data. City data.
    27. 27. Cyclemeter
    28. 28. Copenhagen Wheel
    29. 29. Aesthetic Artifacts Positive reinforcement . Sharing and social perception.
    30. 30. Nike+ Storefront
    31. 31. Mobile Logger
    32. 32. Thank you.

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