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As the adverts would have you believe, there’s not much you can’t do with an iPad: doctors can view X-rays on it, school children can read text books on it, and most importantly you can play a giant game of angry birds on it! But how far do its possibilities extend as a legitimate research tool? By that I mean, how far can they go in replacing standard, much more expensive research equipment?

It is this question which Karl Proctor, a PhD student at Swansea University, looked to answer during his work on the study of body motion (poster). Proctor, along with his advisor Ian Thornton, looked at whether body motion could be successfully studied by using the integrated hardware in mobile devices. “We wanted to see if there is a relationship between the results of studying full-body movement with expensive kit, and device movement with the less expensive kit”, Proctor said. In this case, the less expensive kit happened to be an iPad, but I’m assured any similar technology can be used.

Two apps were designed, requiring participants to carry out tasks of varying difficulty to measure the effects of cognitive load on movement. The iMot app presents participants with randomly moving objects which they must prevent from colliding by drawing paths for them to follow; the difficulty is changed by increasing the number of objects and their speed. Alternatively, the iMath app requires participants to keep the iPad as still as possible whilst being presented with math problems of varying difficulty, they must then move the iPad either left or right to indicate whether the solution to the problem displayed is correct or incorrect. Results from both experiments demonstrated that the iPad could indeed detect small differences in movement between different cognitive loads, implying that it could be a valuable tool to study the relationship between movement and mental processing.

One interesting finding to come out of the experiments was that although there was a difference in the amount of movement between people, within individuals there are characteristic ways in which they moved; for example, Proctor says, “if participant 1 moved more than anyone else in the easy condition, then that same person seemed to also move more in the more difficult condition, and vice versa”.

It is this finding which may lead to some particularly interesting long-term ventures. “Something that I am thinking about is security,” Proctor says, speculating that over the next few years, as phones and other portable devices become ever more powerful and sophisticated, it will be possible to have basic heuristics built into the operating system. The idea involves using neural networks to see if a person can be identified by their movement data from the built-in accelerometer, “so even if someone steals my mobile phone and (somehow) knows the passcode, their movement patterns will be different than my own, which will be detected by the phone’s OS and they will be challenged to identify themselves as me”, he says.

Sounds simple enough, but Proctor notes that there are several issues which would need to be considered, such as the context in which the device is being used, the mood of the person, and the task being done at the time; all of which can affect our movement.

Such ideas may be speculative for now, but if Proctor’s research is anything to go by, we may see mobile devices such as the iPad increasingly featured in scientific research.