This paper presents a comprehensive metrological analysis of the Microsoft Kinect motion sensor performed using a proprietary flat marker. The designed marker was used to estimate its position in the external coordinate system associated with the sensor. The study includes calibration of the RGB and IR cameras, parameter identification and image registration. The metrological analysis is based on the data corrected for sensor optical distortions. From the metrological point of view, localization errors are related to the distance of an object from the sensor. Therefore, the rotation angles were determined and an accuracy assessment of the depth maps was performed. The analysis was carried out for the distances from the marker in the range of 0.8−1.65 m. The maximum average error was equal to 23 mm for the distance of 1.6 m.

The paper presents a technique for measuring membrane displacements with one motionless camera. The method consists in measuring the distance to an object based on one image obtained from a motionless camera with a fixed-focus lens. The essence of the proposed measurement technique is to determine changes of the distance between a membrane and a video camera based on analysis of changes in the focus view of a marker placed on the membrane plane. It is proven that the used technique allows to monitor the frequency and amplitude of the membrane vibration. The tests were performed for the oscillation frequency in the range from 0.5 Hz to 6 Hz and deviations from the neutral position in the range of ±3 mm.

Background: The aim of the study was to answer two questions: 1 – Can data processing algorithms ensure sufficient accuracy for estimating human body pose via wearable systems? 2 – How to process the IMU sensor data to obtain the most accurate information on the human body pose? To answer these questions, the authors evaluated proposed algorithms in terms of accuracy and reliability. Methodology: data acquisition was performed with tested IMU sensors system mounted onto a Biodex System device. Research included pendulum movement with seven angular velocities (10-120°/s) in five angular movement ranges (30-120°). Algorithms used data from accelerometers and gyroscopes and considered complementary and/or Kalman filters with adjusted parameters. Moreover, angular velocity registration quality was also taken into consideration. Results: differences between means for angular velocity were 0.55÷1.05°/s and 1.76÷3.11%. In the case of angular position relative error of means was 4.77÷10.84%, relative error of extreme values was 2.15÷4.81% and Spearman’s correlation coefficient was 0.74÷0.89. Conclusions: Algorithm calculating angles based on acceleration-derived quaternions and with implementation of Kalman filter was the most accurate for data processing and can be adapted for future work with IMU sensors systems, especially in wearable devices that are designated to support human in daily activity.