Mobile Apps, Sensors and Gamification

Oh boy do I have a lot of mobile apps, I just had a look at the home screen of my iPhone and realised I’ve got 7 pages of apps there. I have to admit I probably only use a quarter of them on a regular basis, but the apps that I use regularly are ones that do something unique that often can’t be quite replicated on a desktop computer in the same way. Take for example Sleep Cycle, it’s an alarm clock app that measures my sleep patterns using the accelerometer (and it’s also telling me my average sleep time is 7h 14m over the last 54 nights… not nearly enough sleep for my liking). I also use the native Apple Maps app often as it gives me on the go location and routing information that I need when travelling to new places. It not only shows me my location, but also the direction I’m facing which is very useful. Another app that I really love is RunKeeper. I don’t exercise enough, but when I do I use this app to track my running and I really love it. It works like a breeze, has a beautiful interface, shows me exactly where I’ve been and integrates nicely with the Runkeeper website and Facebook. However, I recently stopped using it. Although though it provided me with exactly what I needed to track exercise it was missing one thing that I never thought I’d need when exercising… Zombies.

I started using Zombies, Run! about 4 weeks ago now instead of RunKeeper. The app doesn’t have nearly as many features as RunKeeper, in fact it has a lot less – I can’t see the route I took, I can’t tally the amount of exercise I’ve done and it only tells me what speed I’m running in min/km which annoys the hell out of me as I’m used to km/hr. But I use it anyway, and the reason being is because it provides a much more engaging running experience for me. I usually just listen to music but with Zobmies, Run! I now have a story play out while I run. I also collect items for my home base and I have to outrun at least two zombie mobs each session and this collectively makes the running experience a lot more enjoyable.

Enhancing the experience using game elements

In the field of human-computer interaction (HCI) making a product that is usable is no longer the sole focus of design. A number of mobile apps, like Zombies, Run!, have turned to video games to inspire them to create experiences for non-game services and applications that engage users in completely different ways. Although the usability of a product plays a very important role in design, over the last decade HCI researchers have encouraged the idea that product design should also focus on the experience that users have with, and from, the product (Roto, 2006). As the focus has shifted to the user experience, research has began to explore games as a means to understand and elicit feelings of play in products and interfaces (Blythe et al., 2004) as a means to make a product more engaging and enjoyable to use.  A study in 2007 by Costello and Edmonds outlines a framework of pleasurable experiences, which was used as a basis for the development of the Playful Experience Framework or PLEX (Korhonen et. al, 2009).  The PLEX framework is derived from studies in interactive art and videogames with the aim of aiding the design of interactive products to “make them more engaging, attractive, and most importantly, more playful for the users.” More recently though research has borrowed directly from games, using elements such as achievement systems (Montola et al., 2009) as a means to enhance the experience of using a photo sharing service; and leaderboards and points to enhance the experience of a medication reminder system (De Oliveria et al., 2010). This use of game elements in non-game contexts such as these has been defined as gamification (Deterding et al., 2011).

Gamification has been explored in both desktop and mobile applications, but to me it’s the latter that provides a really interesting avenue for exploration.  This is because with today’s smart phone technology offer the ability to attach game-like interactions to everyday actions through the use of sensor technology. Mobile apps can use sensor information (e.g. location and time) to enforce game rules that are applied to our everyday interactions to make tasks that aren’t highly motivating to some, potentially more enjoyable and engaging – such as exercise.

Let’s get physical: Sensors, gamification and you

“The most important benefit computers bring to gaming is that the computer relieves the players of the burden of personally implementing the rules. This frees the players to become as deeply immersed in a video game as they can in other forms of entertainment.” (Admas, 2010)

Sensors are important for gamification because the actions we make in the real world (e.g. running) will be used as input for the game, therefore we need someway to implement these game rules. As most applications are meant to be used by thousands of people, employing an individual or group of people to enforce these rules would be near-impossible. There is the option of crowd-sourcing (e.g. The Eatery) but you need a willing community for this. Therefore using sensors, dare I say it, makes sense. Let’s look quickly at the different types of sensors we have at our disposal.

Indulska & Sutton (2003) divide sensors into three groups; physical, virtual and logical sensors.

  • Physical sensors are hardware sensors in devices that can attain physical data regarding the user and their environment such as location, movement or temperature.
  • Virtual sensors source context data from software applications or services such as current computer logins or search history.
  • Logical sensors use multiple information sources and combine physical and virtual sensors to solve higher tasks – an example given is locating an employee by using his current login at a desktop PC and mapping that to a database of device location information (Baldauf, Dustdar, & Rosenberg, 2007).

These different types of sensors can be used to provide us with various contexts that can be used as input for game elements. For desktop and web applications virtual sensors are going to the primary source of context available, as few physical sensors are often included. This might include things like using the number of comments added to a site, the number of words written in a blog post, the number of friends added on social site or if you’ve tweeted about a particular hash tag. These types of contexts are often quantitative amounts – measured easily and often used to power web-based gamification platforms (e.g. Badgeville).

When it comes to mobile devices though developers have access to a much larger range of physical sensors that can provide a much broader range of different contexts. Take the iPhone 4S as an example, it includes a range of sensors including:

  • GPS – provides location data
  • Compass – provides directional data
  • Accelerometer – provides movement, orientation and acceleration data
  • Gyroscope – provides orientation data
  • Camera – provides location data when combined with physical markers such as QR codes. When combined with the camera flash it can also be used to provide blood pressure data.
  • Microphone – provides sound data
  • Light sensors* – provides light data
  • Proximity sensors* – provides proximity of a user’s face to the phone
*not yet available to developers

These sensors on their own open up a range of different potential contexts that can be used as input for gamification. We could use the location information of a person to see where they have been and unlock various exploration achievements, or we could use the accelerometer data to see how much a person moves during the day and translate this to exercise for a virtual pet. It gets even more interesting when we combine the sensors together to infer logical contexts. For example combine location with accelerometer data to get speed and body movement, along with whether today is a work day and if a person is travelling in the direction of their work and we could potentially determine if someone is running or cycling to work vs. travelling by car and reward them depending on how green and healthy their choice of transport to work is.

Let’s take a quick look at Foursquare as a real example. Foursquare is a mobile app that was launched in 2009 and is one of the most famous examples of gamification. In terms of sensors Foursquare uses physical location combined virtual sensor information such as business listings, as well as previous check-in history of the user, friends and other users. These different contexts are used to dish out reward-based game elements, for example check into a location you’ve visited before and you get 1 point. Check into a location you’ve visited three times this week and you get bonus points! Check in with a friend for the first time receive bonus points. Check in to a new place before any of your other friends and receive bonus points. Check in to a place that is a large distance from your last check in and receive bonus points! Check into a location more than anyone else and you receive the mayorship of that place. As you can see with Foursquare it not only uses location information to reward users but also a range of other virtual sensor information as input for the game elements.

I sense a great disturbance in the force – accuracy, cheating and fixes

However when utilising physical sensors there are always going to be some limitations – the biggest being the accuracy of predicting and inferring contexts. For example the GPS in a smart phone might be able to accurately determine location to a couple of metres when outside, but as soon as you’re in a building or a tunnel there are going to be signal issues and the location sensed of a user may be way off. In order to compensate for this we might allow for a larger location radius when determining a person’s location. If this is the case then there is the possibility that people might exploit this increase in radius to cheat which raises an interesting design question, how do we balance having a system that can be exploited and having a system that is usable?

Let’s use Foursquare again as an example because it handles this balance well.  They’ve implemented what they call a fraud detection system in place to make sure checkins are real. Although they don’t specifically say how it works, you can play with the app a little to reveal some of it’s inner workings. To begin with Foursquare will allow you to check in to any place around the world but it will only award points and other game elements if you are relatively close to the location, sensed using location sensors such as GPS. Secondly, check into 3 places immediately one after the other and you’ll get points but check into any more immediately after and you’ll receive no more points (see image).

The issue with this is that it although it picks up on cheaters it doesn’t accommodate for some particular usages contexts of some users. For example if you’ve just been to five different places in the last hour and then remembered that you should check-in to those places, you won’t get as many points for doing it too quickly (see link from the image). This means that this particular user has to adapt to how Foursquare implements the rules in order to be rewarded, which is a little counterintuitive however the utility of checking in is still provided.

There’s a gamified app for that…

As mobile technology continues to be used for more and more applications we can explore how to make the experience of these applications more engaging and exciting by using gamification. Utilising user context via physical, virtual and logical sensors provides us with a way to record actions and enforce game rules layered on top of non-game activities, making sure that players are rewarded automatically for the achievements they make. There may be some accuracy issues that can open up exploits and these need to be considered when designing these types of apps.

Right, that’s a long post and probably enough for now! Final takeaway – gamification for mobile applications is a pretty exciting area to be in right now, make sure to try out some of the gamified apps out there like Zombies, Run! or Foursquare and note what kind of sensors they use and how they handle issues of accuracy and cheating.


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  • Baldauf, M., Dustdar, S., & Rosenberg, F. (2007). A survey on context-aware systems. International Journal of Ad Hoc and Ubiquitous Computing, 2(4), 263–277.
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