AutoBot Presentation for Personal Informatics
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This is the final presentation for the design of Autobot, a smart phone app and built-in dashboard interface that provides information about your driving habits and promotes positive behavior change. This presentation details our design process, AutoBot's functions, and some of the theoretical framework used to explain our design decisions.
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AutoBot Presentation for Personal Informatics
- 1. AutoBot April 10, 2012
- 2. Introduction Mission of Autobot Influencer Matrix Research Solution Autobot Demonstration Video, Design defense, Relevance to course concepts, UI Standards
- 3. Mission of Autobot Problem: People give little Mission: thought to how well they are 1) Design a system that informs driving because it is people of their driving habits ingrained, habitual behavior. 2) Leverage Lack of awareness and gamification, information overconfidence prevents people visualization, and social influence from improving their driving to promote positive behavior habits, which can lead to change unsafe, costly, and environmentally harmful driving.
- 5. Research Personal Interviews Personas and Scenarios Survey Comparative Analysis User Testing Competitive Apps
- 7. Survey SI Distributed 126 responses Thanks! What direction should we take? Driving behavior? Maintenance reminder? What do people want to know about?
- 8. Survey: Important Results 89 % 43 % 40 % Know What’s Improve driving more wrong with habits about their my car? habits
- 9. Comparative Analysis Competition thought of many ways to provide driving feedback Easy to understand metrics for any driver Acceleration Braking Cornering
- 10. Testing the Competition 5 volunteers drove around with “Driver Feedback” Impressed with the simplicity of app Enjoyed seeing where on their driving path they made mistakes Did not feel motivated to improve
- 11. Solution Multi-platform 3 Key Metrics Behavioral Change Techniques
- 13. In-Car App
- 14. Console Design myFord Touch Toyota Prius
- 15. Console Design, cont’d Autobot
- 16. Functions Minimized Summary
- 17. Login
- 18. Login Limited barriers to entry Simple sign-in & added social influence
- 19. Login: Facebook Easy out Simple language of what will be posted on FB What information is required of you
- 20. Settings Fine grain Personalized control of settings for social user-specific media feedback options
- 21. Record a Trip (video)
- 22. Record a Trip App records driving data using the phone’s accelerometer, gy roscope and GPS sensors. The device must remain stationary while recording.
- 23. Self-Report Shows perception vs reality to reveal misperceptions
- 24. Summary, Maps and Alerts (video)
- 25. Summary Average Points Contextualization: Shows impact of driving on fuel economy and environment Praise and advice based on your data
- 26. Map Contextualization: Shows where user drive well (green) and poorly (red) Alerts: Shows specific areas where the user drove poorly
- 27. Alerts Problem specific advice Contextualization: Shows the cost of wear and tear incurred by mistakes
- 28. Trip Log and Compare Trips (video)
- 29. Trip Log Users can toggle Routes to see how History tracking: routine trips change User can pick and over time choose previous trips to view Users can retrieve old data and reflect on past mistakes
- 30. Compare Trips Users can juxtapose data from two trips to see how they compare
- 31. Graphs (video)
- 32. Graphs Trends over time Simple and Clean Design Impact
- 33. Social (video)
- 34. Social: Profile Social influence Earning awards for driving behavior
- 35. Social: Groups Group inclusion aligned by common interests, location, … Gamification
- 36. Green and Tips (video)
- 37. Green Eco Status Level Progress Visualization Eco Points History
- 38. Tips Simple instructions Benefits clearly laid out Social influence Clear monetary incentive to partake in this Earning points activity
- 39. Conclusion Gamification Users receive points based on their driving behavior and car maintenance Leaderboards and groups provide an engaging, competitive environment Visualizations display progress toward goals and new levels
- 40. Conclusion Information Visualization Stats pages display both broad and specific views of user’s data Maps and Tips contextualize data to be more compelling Graphs allow the user to compare variables Self-reporting reveals misconceptions about behavior
- 41. Conclusion Social Influence Users can customize their profiles, giving them ownership of their data and progress Leaderboard publishes the user’s data to their friends pages, making them more conscious of their driving
- 42. Questions?
Notas do Editor
- Everyone interviewed someone on their driving habits, which helped us create the following personas. We wanted to think about people who may use this app to improve their understanding of their own driving habits and also have a desire to learn more about being a conscious driver, to people who already take good care of their cars, and want a way to track information. We accounted for a large range of domain knowledge around cars by allowing users to choose how engaged in the system they want to be.
- Multi-PlatformA mobile and in-car application that informs drivers of their driving behavior based on three primary metrics: acceleration, braking, and cornering3 Key MetricsAcceleration, Braking and Cornering and fuel economy to a lesser extentBehavioral Change TechniquesThe mobile app informs and motivates users by taking advantage of behavioral change techniques. The app provides structure where was none before incentivizing positive driving behavior using a points system, offering status titles, easy to understand personalized feedback, and utilizing social influence through the AutoBot online community and other social networks.
- This technology is still emerging, but we wanted to integrate into car’s existing dashboard functions. As you can see, this is how two of the major players in the automotive world present their consoles.
- We limited the functions to summarizing the information listed, and to minimize the score so that the driver could use other functions. We didn’t include other tools available on the phone application because we didn’t want to distract the driver any more than was necessary, especially since that was a lot of the criticism levied at current car console interfaces. That’s why we included the more interactive and engaging functions directly in our phone application.Here, we begin directing the rider by displaying the current score and giving the users a glimpse of how well they’re doing. This provides structural support in the form of points and motivation in terms of giving the user a guage of how well they’re doing on a 100 point scale.
- Nesser’s Ecological Self: Self-reporting shows how you perceive yourself, and then Autobot juxtaposes your perceived driving performance with your actual driving performance
- Nesser’s Ecological Self: Self-reporting shows how you perceive yourself, and then Autobot juxtaposes your perceived driving performance with your actual driving performance
- Nesser’s Ecological Self: Self-reporting shows how you perceive yourself, and then Autobot juxtaposes your perceived driving performance with your actual driving performance
- Personal Motivation (Influencer matrix component)The graphs address the personal ability barrier. These data visualizations allow the user to determine whether or not changes in her driving behavior are having an effect on the quality of her driving, be it positive or negative. For example, if a user decides to follow the advice provided with one of her alerts she can determine if that change in behavior has had an impact on any, or all, of the driving metrics by referring to the line graph.Direct the Rider (Rider and path)Although the graphs do not direct the rider in and of themselves, they are a key part of that goal. Telling someone to drink 2% milk, or in this case slow down as they enter a turn, is important but it is also important to give that person feedback on their progress toward change. So I’ve been drinking 2% milk for a month now, did that really make a difference and if so, how much of a difference? Graphs, and other data visualizations, can help answer these questions and in doing so motivate a person to continue to change their behavior.Simple and Clean (UI standards)For the graphs I wanted the data to be presented to the user as clearly as possible. Although a more artistic representation would have done more to motivate the elephant I was focused on directing the rider. If you want to make a data visualization easy for a person to decipher you need to make the visualization simple and clean. This is a common practice in the field of psychology, to avoid using things like 3-D bar graphs and other more complex visualizations when a simple 2 dimensional graph could have presented the information just as well. Something to keep in mind is that our user base will be rather broad and that all of them will not be able to find meaning in more complex visualizations.That being said we’ve given the user a line graph that allows her to observe changes and trends in her driving behavior over an extended period of time. The scatter plot below allows the user to identify correlations between her scores on the three metrics and her fuel economy. She can determine the impact of a score of 70 on acceleration versus a score of 95 in terms of fuel economy, something that should be meaningful to most users. It is our hope that when the user observes the negative impact of her poor scores on her fuel economy she will be further motivated to improve her driving behavior.
- Structural Motivation (Influencer matrix component)This visualization is meant to help break down the barrier in structural motivation. Watching her tree grow provides the user with an immediate, and visible, reward for her eco friendly behavior. Aside from watching her tree grow the user can also track her progress toward obtaining the next level of proficiency in her eco friendly behavior.Motivate the Elephant (Rider and path)The tree visualization is meant to motivate the elephant. Unlike the graphs, with visualization we wanted to appeal to the user’s emotional side and therefore went with a more artistic visualization. Simply providing the user with numbers to demonstrate her environmental impact didn’t seem like it was sufficient to quantify diverse data. A common unit by which environmental impact is often measured are pounds of carbon saved. Now I don’t know about you but that unit is meaningless to me. So for our visualization we chose something familiar to people, one that they immediately identify with the environment, a tree.Design to Evoke Emotion (UI standards)For this visualization the I felt that the most appropriate choice would be a more visually appealing and somewhat atypical design. When trying to evoke emotion in a observer it is more important to provide an interesting visualization than one that is clean and simple, such as the graphs presented earlier.