High-Fidelity Prototype Implementation
March 31, 2010
What We Want to Visualize:
We are going to build a system to help monitor the activities of outdoor hikers. In our particular scenario, the users of the data visualization are not the hikers themselves. The hikers do carry the phone that collects and reports the data, but the end users who see the visualization are people watching out for the safety of the hiker as he travels across risky or dangerous terrain.
Usage Scenario:
When the hiker goes out to pursue a mountainous adventure, he carries his mobile phone with the built-in sensor set to ‘on.’ The person viewing the visualization, whom we will refer to as the saver, collects the sensor data from the hiker’s phone, and uses the data visualization to help him understand the hiker’s contextual information. This contextual data provides clues necessary for the saver to locate and assist the hiker. For example, the saver may find that the hiker is engaging in dramatic or risky movements based on readings from the accelerometer sensor. Using this contextual data, he may report to the hiker and provide suggestions as to where stable and steady terrain is located, based on information collected with the GPS sensing device. The GPS data will be shown using a histogram or an accumulation of lines. The saver may be monitoring several hikers at the same time; accordingly, we try to make the visualization data accessible pre-attentively.
Tools We Are Using:
The tools we will use to process and visualize the data are JSP + Java. Java will be for the system’s back-end, and will collect sensor data remotely from the mobile phone. JSP will be used for the system’s front-end, to build the visualization. Here, we may also use JavaScript library to help with the visualization, for example, ProtoVis and/or Google Map API.
The Source of the Data:
The entire architecture of our system has been set up. The mobile client, server, and desktop client work together to pass data. We are currently able to collect live sensor data from our device.
How We Might Evaluate the Final Result:
We will actually deploy this to see whether the saver can interpret the hiker’s contextual information correctly, though we are not going to travel to any mountainous areas.
Role Division:
Jessie and Gary have collected research data on the ways in which sensor data can be helpful to users. They also made the prototypes based on our group discussion. Zhenan and Sang have been exploring the technical requirements to build the system. They constructed the back-end data capturing functionality, which is ready for the purpose of visualization.
High-level Plan:
We need to explore how to present the sensor data to the saver so that he can actually help the hiker. We also need to decide how to visualize both live contextual data alongside historical contextual data. So far, we have determined that the saver can assist the hiker by viewing and communicating about terrain quality and distance, weather conditions, geographical coordinates, time, and light intensity. The usefulness of data like this is self-explanatory; essentially, this data is helpful in the sense that it provides information about the hiker’s context from a high-level, comprehensive viewpoint, and as such can make hiking safer and more enjoyable for explorers.
