Samenvatting

The goal of the project was to study various three-dimensional motion tracking methods and to conclude on the system that can be implemented with Philips hair styling products. The assignment includes tracking such motion characteristics as: orientation, speed and position of the appliance. For the hair styling application the desired technical solution should also provide tracking motion of the appliance relative to the motion of the user’s head.
The research and comparison of possible motion tracking techniques was performed and concluded that inertial – vision tracking is the most suitable solution for the given application.
Literature research, theoretical estimations and tests were performed in order to answer on to what extend inertial tracking can be used for motion tracking of the appliances. The main source of position and speed errors in inertial tracking is the inaccurate gravity compensation that is the result of orientation measurement error. Theoretical calculations were made that show possible accuracy of inertial speed and position tracking depending on the orientation errors. The tests for orientation, speed and position tracking were made with the chosen low-cost BNO055 inertial measurement unit that integrates sensor fusion algorithm and automatic sensor calibration. The tests showed that orientation tracking with the sensor is accurate with the error lower than 0.06 degrees. Based on both estimations and performed tests it is concluded that speed and position tracking with inertial sensors even with tracking time of under 10 s is not feasible for the application. The performed tests and research are important, because they allows to understand what accuracy of motion tracking can be expected from different sensors, especially taking into account the fact that inertial modules are currently being very rapidly developed.
Vision tracking with application of Microsoft Kinect depth camera is chosen to be the main solution for the application. Kinect skeleton tracking functionality is used to localize the hand of the user and therefore conclude on the positon of the appliance. A set of tests were performed to check the accuracy of the Kinect position and speed tracking of hand and hand holding the appliance. The tests were made with a hand attached to the test equipment set to move with the speeds of 0.5 [m/s] and 0.05 [m/s]. The test results are presented in the chapter 5, tables 5.7 and 5.8.
As a result of the project of the formulated research question were successfully answered. It is currently not feasible to integrate the developed technical solution with products on the market. However, based on the project results it is possible to implement the motion quantification set up that would allow to analyze the user behavior at the product research center.
The main recommendations for the next steps of the project would be to implement an algorithm for separation of hand and appliance by color that would have minimum influence on the frame rate. Combining the separation algorithm with more advanced filtering that currently used will allow accurate speed tracking of the hand holding the device. Detailed recommendations are presented in chapter 6.