Dual quaternions and dual quaternion interpolation are powerful mathematical tools for the spatial analysis of rigid body motions. In this paper, after a review of some basic results and formulas, it will be presented an attempt to use these tools for the the kinematic modeling of human joints. In particular, the kinematic parameters extracted from experimentally acquired data are compared with those theoretically computed from dual quaternions rigid body motion interpolation.

This paper presents results of the study devoted to analysis of impact of upper extremities' momentum on the jump length and analysis of selected kinematic data changes during the standing long jump. Four young sportsmen participated in the initial study. They have performed standing long jump in two measuring conditions: with and without arms swinging. Motion was captured using a 3D opto-electronic camera system SMART (BTS) and selected kinematic data were evaluated using software packages and data processing: trajectory of body centre of gravity (COG), velocity of COG, maximal vertical distance of COG, take-off angle together with momentum of upper extremities were analyzed. The data were statistically evaluated using descriptive statistics and analysis of variance. Statistical significance of the kinematic data and jump length were analyzed using the Kruskal-Wallis test and post-hoc test (p<0.05) in Statistics toolbox of Matlab program. Statistically significant differences were assessed within intraindividual and intraclass comparison of data.

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.