Visualization and Target Users:
We are going to build a system to help monitor users’ activities outdoors, particularly in mountainous areas. The system displays information including trail records, speed, ambient light intensity and weather forecast (See prototype figure below). The intended target audience consists of people whom we are calling “savers.” These individuals monitor the hiking routes of people out trekking in the field, while collecting data about both the person and contextual surroundings. The savers can communicate with hikers about safety and precautionary data, such as flood warnings and rock slides, and they can help guide the hikers back to the appropriate trail should they wander off into the wilderness. With the information the system provides, savers acquire an understanding of the past and current positions of the hikers and the surrounding environment.
How we’re going to visualize it:
The system will be built as dashboard style/ the visualization mock-up is on our website, and is also shown below. For the implementation, we will use JSP + Java to make the system. JAVA will be used for the back-end of the system to get sensor data remotely from mobile phone. JSP will be used for the front-end of the system’s visualization. Here we may use one or more JavaScript libraries to help with visualization, for example, Protovis and the Google Map API.
High-Fidelity Prototype for Mountain Hiker Contextual Data
Description of the Prototype’s Interface:
The image directly above in the interface mock-up is a satellite image from Google Earth depicting the Glacier Point hike to the top of Half Dome in Yosemite National Park, California, U.S.A. This destination was selected both because as a group we possess familiarity with the layout of the park, and because this particular trail is known to be long, arduous, and challenging. Dangers abound on long, strenuous hikes, and this sort of challenging situation is the type for which we are designing this system. The starting point of this trail is in the lower-left, displayed as a blue bubble with a star in the middle. Similarly, the end point, on top of Half Dome, is also marked with a blue bubble containing a star. The hiker, shown on the map in blue, is working his way down the trail in this view. The data on the right are, from top to bottom, speed/acceleration, ambient light, a time line slider to retrieve recent data, and a weather display for the conditions throughout the day.
The Source of the Data:
The entire architecture of our system has been set up. The mobile and desktop clients work together with the server to pass data back and forth. We are currently able to collect live sensor data from our device. We will also collect terrain and route data from Google Earth and Google Maps. The sensors will collect data on acceleration, orientation, light intensity, and GPS readings, and weather and forecast data will be regularly updated.
Evaluation of 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 at the current time. Realistically, given Michigan’s lack of exciting hiking trails in the Southwest part of the state, we may evaluate our result by tracking one another’s movements in a place with lots of hills, such as the UM Arboretum.
Division of Labor:
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. Also, they will utilize Google Earth/Maps API to present the information to users.
High level plan to complete our project:
We need to explore how to present the sensor data to the saver so that he can actually help the hiker. We need to figure out how to visualize both live contextual data and historical contextual data. We would also like to explore the ways in which the saver can communicate with the hiker. The obvious modes of communication are the cell phones and possibly a Bluetooth device. We would also like to figure out how Google Earth/Maps might be used to scan the paths ahead of the hiker for potentially dangerous or impassible road blocks. We would like to collect data to point out refreshment areas and distances (to guard against dehydration), and to anticipate rest stops (to avoid pulled muscles). Of course, with the high-level overview of the hiker’s route that the saver has access to, he can also recover the hiker’s last route, and the safest route back to the main trail should the hiker become disoriented or lost.
One of the most substantial and obvious challenges that we will have in designing a system intended for monitoring wilderness hikers is that we do not have wild areas within close proximity. Since we cannot at this time collect data from travel through actual mountains with risks, challenges, and dangers, we will have to devise another scenario that will approximate the target conditions as closely as possible.
First Report on the Hi-Fi Prototype (See Update Above)
In the time spanning from the submission date for the lo-fi prototype to the deadline for the hi-fi prototype, our ideas have evolved into a different design project. We have identified the target audience as hiker monitors, or people who are watching the progress of a hiker while collecting contextual data from the hiker’s mobile phone and viewing the data on a large-screen PC.
The mock-up of our interface is displayed below. The left-hand side shows a Google Earth map of Yosemite and Half Dome, and the right-hand side contains data visualizations for speed, ambient light, and weather conditions. Time is represented as a scroll bar, as well.
High-Fidelity Prototype for Mountain Hiker Contextual Data
When you click on the image above to view the screen-sized interface, several features become apparent. The visualization in the form of a satellite image of a trail hike in Yosemite National Park is outlined in red on the map. The hiker’s current position is displayed with the icon of a man carrying a backpack. On the right-hand side are data visualizations for the hiker’s speed, the ambient light, and the weather. Positioned in between the weather and ambient light displays is a slider that can be used to adjust the time. In other words, by sliding the bar, data from a time frame spanning 24 hours can be viewed in the graphic displays.
Mobile Phone Information Visualization 