Success, adrenaline, fame, the emotional impact of winning and losing, a healthy lifestyle, socializing, the thrill of pushing your own limits, monetary reward…there are many reasons for practicing sports. Which of them motivates a person most is an individual and personal question. But in case improvement of one’s own performance, or the extension of one’s personal skills are concerned, the question often comes down to the influence of two variables: (1) the own personal will and mental ability to endure the hardness of training and effort, and (2) the availability of an optimal training environment that facilitates the learning of high-quality motor skills.
Personally, I can’t help you with the first one (but there are plenty of mental trainers, sport psychologists and motivation coaches in this world that are happy to do so). However, I might be able to help you with the second one: with the development of wearable motion sensing devices over the last few years, possibilities for technology-driven motion analysis increased drastically. Sensors can now be employed to obtain new and more ubiquitous motion information. This motion information (or motion feedback, as it is often referred to) can then in turn support you in better understanding your own performance. And this improved understanding might in the end result in you showing better performances in future. Sounds nice, right?
Unfortunately, it also sounds much easier than it is in practice – both wearable sensor data as well as actual motion performance data tend to be very complicated to use. The intention of this page therefore is to help you with the sometimes tedious, and always time-consuming process of system implementation. To help you develop your own augmented motion feedback system in less time and with less loss of nerves. And hence bring your own performances, or the performances of somebody else, to a new and yet unknown level.
Implementing Augmented Motion Feedback Systems
Developing an augmented motion feedback system, you are likely to encounter a bunch of questions that wants to be answered. These are generally independent of the intended application type and chosen sport, as for example: how can one employ the most recent sensor technology for sensing a sports performance? How can one transform captured motion data into deeper numerical representations? How can one retrieve and identify meaningful information from these data representations that could not be discovered otherwise? And how can such information be displayed?
Answers to those questions generally follow a semantically logical sequence. This consists of the collection of data, the augmentation of collected data, the transformation and sense-making of data and the acquisition and provision of relevant feedback information:
On this homepage, relevant information to quickly run through all processing steps was gathered. Furthermore, possible applications are described to inspire you in your own work – for a broader future support and enhancement of human motion performance.