Interaction with IoT data to help users train smarter

Sammanfattning: It is becoming more and more common to exercise regularly, and with this new fitness trend follows more exercise applications to our smartphones. Applications aiming to help us exercise by structuring and logging our training. The user experience for these applications can be a little bit dull, since most of the applications depend on the user following instructions and entering input of the result manually. The relatively new technology Internet of Things (IoT) seems to be a well fit solution to solve the issue of manual logging of a user’s training. Doing this with the help of sensing the users exercise motion and then providing the result to the user. But as with many other IoT projects, not all data collected from the IoT devices are fully utilized. New interaction possibilities can be created, which gives better user experience as well as better health by helping the user train smarter and safer. A study was performed regarding the IoT data from Sony’s project, Advagym, which tracks machine exercises movements to log a user’s training. The study was aimed to find new applications for the IoT data, which is not currently being utilized by the Advagym system, to potentially being able to increase the user experience. Using a user centered design process, multiple iterations of prototypes was developed, which collects and presents real-time data from the IoT units. Using velocity based training (VBT) methods, the aim was to with the help of the prototypes, get users to follow a predefined velocity target for an exercise movement. The prototypes were developed as iOS applications in Swift which listens to Advagym’s IoT units’ broadcasts, with data packages which provides data of the result for the user’s performance. Three prototypes were tested with 50 participants to be able to benchmark performance differences, as well as testing the usability and understandability of the prototypes. The tests show that with the help of continuous feedback on the users’ performance based the velocity, the user can achieve an exercise tempo with low tolerance for errors. This can further help a user to train smarter and safer, based on their training goals.