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Viderefit: A Fitness Tracking App for Android Tablets

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Earlier this year I talked a bit about how I wanted to do some Android development to broaden my skill set. A little after that post I finally joined the 21st century and got an Android smartphone. Over the course of the summer I recorded all of my hikes and bike rides using Google’s My Tracks app. With my season coming to a close, I began to think about what I could do with all this data that I’d collected and what kind of insights I could discover. As a result, I came up with Viderefit, a simple Android tablet app, that allows me to review my changes in my performance over time. In this post I’m going to go over the product design cycle that went into making this first phase of the app- from brain storming, requirements building, user interface design, development, and post-production. I’ll be finishing up with some thoughts on the next set of features I’ll be contemplating to make the app commercially viable on Google Play.


For the first phase of the project, I set out with a few simple goals that I wanted to focus on:

  • Since I’d been focusing research projects lately, I wanted to return to my roots and focus a bit on user experience and interface design. As a result, I decided It was important to me that I create an intuitive to use and visually appealing user interface that utilized a number of appropriate and meaningful information visualization techniques.
  • I’ve done a lot of C# and Haskell development lately, and I wanted to do something relatively new, so I decided that I would develop on Android and dust off my Java skills from my college days.
  • I wanted a “quick win”, something that I knew that I could complete over the course of a couple weeks. As a result, I decided that I would spend two weeks planning the project starting in mid-September, followed by another two weeks of development wrapping up mid-October, and the remaining two weeks of October to put together this post for a November publication.

Brain Storming

In thinking about what exactly it was I was going to build I began to ask myself, what questions should I be asking myself. So, I opened up Word and began typing out a bullet point list of questions to understand where I was going with this project. First thing I began to think about was what exactly is physical fitness? What exactly is being measured over time to show improvement? What exactly is improvement? I had some ideas from my experience, but nothing formal, so like anyone else, I jumped Wikipedia and came across the following quotation on the topic’s page:

Physical fitness has been defined as a set of attributes or characteristics that people have or achieve that relates to the ability to perform physical activity. – Physical Activity and Health: A Report of the Surgeon General

Not being completely satisfied with this, I looked around a bit more and found several pages outlining five areas that constitute physical fitness: aerobic or cardiovascular endurance, body composition, muscular strength, muscular endurance and finally, flexibility. Having felt like some progress was made, I moved on to the next question pertaining to measurements. Researching each of the five areas yielded some insights in the types of tests and measurements being used to assess these abilities such as VO2 max, BMI, ROM, S&R and a whole slew of alphabet soup measurements that I unfortunately did not have access to nor were they obtainable from the available set of data.

Thinking a bit more about the data that was available to me, it was clear the only area of physical fitness I could focus on was aerobic endurance. Despite the fact I lacked sufficient data to derive some of the formal measures of physical fitness, I could derive some common sense measures to relate my performance over time. Am I going longer, going further, going faster as I got deeper into my season? Is my improvement uniform over time or did I hit any plateaus? And so on. To explore these ideas, I exported all of the My Tracks data from my smartphone to a SD Card and combined the results using a throwaway C# application and loaded the combined CSV file into Excel.

Left to right: Plot of total time vs total distance, distribution of time spent at a given elapsed time and monthly total time.

Based on my explorations in Excel, I decided that I had the data I needed to answer the types of common sense question I was thinking about and decided what I was after was three different views of my data: a summary dashboard, performance reporting and a raw view of the data.


In deciding on my requirements, I thought a bit about what I had read in Ben Fry‘s Visualizing Data: Exploring and Explaining Data, that exploring most data sets consists of acquiring, parsing, filtering, mining, representing, refining and interacting with the data set. Keeping that in mind, I decided that I would likely have a series of tabs providing different views of the underlying data and sets of tools on each tab to assist in that data’s interpretation.

The summary dashboard is about capturing the “big picture” of the underlying data. In thinking about what was important to me, I wanted to capture how far I’d gone, how long I’d spent and how many times I went out. Each of these three sections would be broken down into a total, a percentage of some reference figures (e.g., the distance between cities), a chart showing the total broken out by activity type and month, box plot showing the underling distribution, a stacked bar chart showing the underlying activity distribution and finally the longest, furthest, or most common track was to be presented.

Performance reporting is about enabling the end user to explore the underlying data. In essence, enabling the end user to chart different features plotted against one another and summarized according to some scheme. The user would then be able to filter by activity type and break the data set out into pre-season, mid-season and post-season components to better see trends taking place over time.

Finally, the raw view of the data provides a listing of all the tracks that were captured. Selecting a track displays a speed and altitude plot along with box plots for speed and altitude for the track in addition to box plots for the season so that the user can compare how a particular track compares to seasonal statistics.


With an idea of the type of requirements that would be needed, it is time to flush out what the user interface will look like and how the user will interact with it. There are a lot of great products out there for mocking up user interfaces, I’ve used Balsamiq and really enjoyed it, but decided for this project, I would keep things simple and just mock things up in Photoshop since it’s easy to work with and low fidelity designs are all that’s needed at this point.

The summary dashboard incorporates the requirements into a vertical three panel design capturing distance, time and frequency of the underlying data. The dashboard is meant to be looked at and not interacted with, as a result the only thing the end user can do at this point is click on other tabs.

Season dashboard mockup.

Bulk of the features in the application will appear on the performance reporting tab. The user will be able to select x-Axis, y-Axis features and y-Axis feature aggregation strategy in order to plot out the results in the right-hand chart area. Beneath the selection criteria are checkboxes for splitting the data out in to full season, pre-season, mid-season and post-season components. Finally, the user can filter out different activities by checking each activity for exclusion or inclusion.

Performance reporting mockup.

The view of the raw data is to provide a view outlining all of the user’s tracks. Each track listing includes the name of the track, date, length, duration and a altitude sparkline. Clicking on a track reveals a speed and altitude plot along with the box plots described in the previous section.

Raw data view mockup.


Based on the planning done earlier in the project, development was a matter spending some time in IntelliJ, translating the design into the appropriate Android conventions and implementing the necessary logic to calculate various statistics and data calculations.

Much of what was needed to implement the application was available in the JDK and Android SDK, however there were a few areas I felt I could leverage existing open source libraries without having to roll my own solution (especially given the timeline I had decided upon):

  • For charting I decided to use achartengine (1.0.0) since it looked to be the most stable and used charting library for Android.
  • To parse the CSV file containing all of the track information, I went with opencsv (2.3) since it seems to most widely used. Although it does look like an Apache Commons CSV package is in the works but not yet final.
  • Since the time and date handing in Java is embarrassingly lacking in JDK 1.6, I ended up using joda-time (2.1) for presenting end user friendly date and time representations.

The three libraries are all licensed under the Apache License 2.0

In terms of code organization and events that take place, the application is structured around Android’s fragment approach to deal with having to provide different views based on the device being used. Since the focus of the application was to develop a tablet application, no additional layouts were developed to support smaller screen sizes. The main activity consists of loading data from an SD card and creating handlers for tab events for each of the three tabs. Each individual tab consists of a replicated paradigm of master-detail fragments and additional settings that are passed between fragments as bundles whenever an end user event takes place.

Application overview. Relationship of fragments to views, flow of data based on events and underlying packages used.

The referenced packages: common, controls, reporting and serialization contain classes for binding views to data, data aggregation (essentially a watered-down version of .NET’s LINQ and Haskell’s higher order functions), and classes for loading track data into memory.


With development complete, I set out to do some polishing and make the application visually consistent. To start things off, I wanted to settle on a color palette for the application. This was done by sampling the HSB space on 60 degrees increments of hue offset by 0, 15, and 30 degrees of hue, with fixed 100% saturation and 80% brightness giving a vibrant 24 color palette to work with.

Color palette and derived color scheme for various parts of the user interface.

Once the color scheme was established, it was a matter of going through the application and making sure each user interface element was using the correct color resource. Once the colors were applied, I focused on applying a consistent spacing between all of the UI elements- specifically 16 dp and 8 dp depending on the context and bordering between components. From there, each chart was examined to make sure that the axes units and labels were presented correctly. One gripe about achartengine is that I was unable to find a way to set the axis title’s padding, so there is some overlap between the axis value labels and the axis title.

With the application spruced up, it was on to icon design and selection. For the application icon I decided to to do a simple vector-based tri-folded map with an overlaid panel and chart.

Lef to right on white and black backgrounds: Initial icon, overlaid icon and final icon.

For the icons to be used in the application, I used those found in Google’s My Tracks app since those icons are available under a Creative Commons 3.0 BY-SA license and represent the vast majority of data that would be imported. Worth noting that most of those icons are based on the National Park Service’s Map Symbols Collection. Future versions of Viderefit will likely switch over to the NPS set since they represent a more considerable collection of activities and the collection is under a public domain license.

Top to bottom: NPS Map Collection icons vs. Google’s My Tracks icons.

Last thing to settle on was the name of the application. During development the name was simply “My Track Visualizer”, but I wanted the app to be more than that, so I decided on “SeeFit” or “cFit”, whichever happened to be available. After a Google search, neither were available so, I decided to use the Latin word for “to see”, Videre, and luckily “Viderefit” didn’t show up in any Google search results, so presumably nobody else has taken it.

End result

After finishing post-production, the application was looking much more consistent and polished. Below are screenshots of each tab taken from my 10.1″ Acer Iconia Tab A500 development device.

Summary dashboard.

Performance reporting. x-Axis options: Distance Traveled (m), Time Elapsed (s). y-Axis options: Altitude (m), Bearing (deg), Distance Traveled (m), Time Elapsed (s). y-Axis aggregation options: Count, Maximum, Mean, Median, Minimum, Total.

Raw data view.

Future Work

In thinking about what it will take to get this product market worthy, there are a few things that stand out in my mind:

  • Data importing – for this project the data resided on an external SD card and was loaded into memory from a parsed CSV file. Ideally, the end user should be able to import data from multiple applications and in varying formats such that the data is stored more efficiently using the built-in SQLite capabilities of the platform.
  • Data exporting – one open question is how this application fits into the broader fitness application ecosystem and what exportable data is of use to other applications.
  • Territory – the project omits any presentation of actual GPS data recorded during each session. For those individuals who are more interested in where they’ve been over their measurements, it seems a territory view would be a selling point. In addition, some form of integration done with the Google Maps API would also help visualize territory and speeds on the map over time.
  • Additional screen support – right now the application was designed specifically for tablets, but other users may wish to use this application on their smartphone.
  • Goal support – having reviewed other applications on the market, the idea of goal tracking is a recurring theme. To make the product more marketable, it would be useful to add in this kind of functionality.


Reflecting back on the goals I set out at the beginning of the project, I feel I made an intuitive and easy to use product consisting of appropriate information visualization techniques; worked on a new platform other than my typical C# and Haskell environments; finally, managed to complete the project according to the timeline I established by finishing all work a full two weeks sooner than originally scheduled. Working on a new platform and hardware was a fun experience. With the work that I’ve done, I feel that I can proceed onto the next phase of the project and determine how to get the application ready for the marketplace and hopefully bring in some revenue in the process.


Written by lewellen

2012-11-01 at 8:00 am

Posted in Projects

Tagged with , , ,

One Response

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  1. […] projects related to clustering GPS trajectories; put some time into my fitness tracking application Viderefit; and put more time into GPU-based speech recognition system I’ve been steadily working on. As […]

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