Dance quantification with Kinect : Adjusting music volume by using depth data from a Kinect sensor
Christoffer Brodd-Reijer · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2012
Humans interact with computers in many various ways. Different interaction models are suited for different situations and tasks. This thesis explores the motion based interaction made possible by the Kinect for Windows device. A stand-alone library is built for interpreting, analyzing and quantifying movements in a room. It highlights the complexity in movement analysis in real time and discusses various algorithms for interpolation and filtering. The finished library is integrated into an existing music application by expanding its plugin system, allowing users to control the volume level of the playing music with the quantity of their body movements. The results are evaluated by subjective interviews with test subjects and objective measurements such as execution time and memory consumption. The results show that it is possible to properly quantify movement in real time with little memory consumption while still getting satisfying results, ending in a greater incentive to dance.