This study focuses on evaluating a system containing five different mappings of physical movement parameters to auditory parameters. Physical parameter variables such as size, location, among others, were obtained by using a motion tracking system, where the two hands of the user would work as rigid bodies. Translating these variables to auditory parameter variables gave the ability to control different parameters of MIDI files. The aim of the study was to determine how well a total of five participants, all with prior musical knowledge and experience, could adapt to the system concerning both user generated data as well as overall user experience. The study showed that the participants developed a positive personal engagement with the system and this way of audio and music alteration. Exploring the initial mappings of the system established ideas for future development of the system in potential forthcoming work.

The goal of this project was to make a shirt with three embedded IMU sensors (Inertial Measurement Unit) that can measure a person’s movements throughout an entire workday. This can provide information about a person’s daily routine movements and aid in finding activities which can lead to work-related injuries in order to prevent them. The objective was hence to construct a sensor fusion framework that could retrieve the measurements from these three sensors and to create an estimate of the human body orientation and to estimate the angular movements of the arms. This was done using an extended Kalman filter which uses the accelerometer and magnetometer values to retrieve the direction of gravity and north respectively, thus providing a coordinate system that can be trusted in the long term. Since this method is sensitive to quick movements and magnetic disturbance, gyroscope measurements were used to help pick up quick movements. The gyroscope measurements need to be integrated in order to get the angle, which means that we get accumulated errors. This problem is reduced by the fact that we retrieve a correct long-term reference without accumulated errors from the accelerometer and magnetometer measurements. The Kalman filter estimates three quaternions describing the orientation of the upper body and the two arms. These quaternions were then translated into Euler angles in order to get a meaningful description of the orientations. The measurements were stored on a memory card or broadcast on both the local net and the Internet. These data were either used offline in Matlab or shown in real-time in the program Unity 3D. In the latter case the user could see that a movement gives rise to a corresponding movement on a skeleton model on the screen.