The paper presents the results of work on reducing the motion data dimension obtained by motion capture system with inertial microelectromechanical sensors. In the beginning, it is given which movements were chosen and rationale for everyone's. After that, assumption is presented about independence of data flows for different types of sensors is checked. Next, method for sorting the incoming data stream for the wavelet transform is considered and method for redundancy decline through finding strong linear relations between data streams is given and scale of wavelet transform is justified. Then method for compressing motion data using a wavelet transform is proposed. After that, it is shown that after data processing, data power spectrum decreases in the high-frequency region.

Keywords: MEMS, motion capture, spectral analysis, wavelet transform, correlation analysis, accelerometer, gyroscope, inertial sensor, motion control, digital motion reference pattern

The article presents results of processing motion data obtained on a motion capture system using inertial microelectromechanical sensors (MEMS). Hardware and software complex compares reference movements with those that operator is performing. Reducing the feature space in motion model is an important task in the context of using many similar sensors that need to be processed on low-power devices. The main way to obtain the distance between two patterns is algorithm of dynamic time warping which has a computational complexity of O2, which means that it is expedient to select features. Statistics of distinction degree between different types of movements are provided to assess the quality of simplified models. Сomplexity of resulting models was demonstrate on Kohanovsky method.

Keywords: MEMS, motion capture, correlation analysis, wavelet transform, dynamic time-warping algorithm, accelerometer, gyroscope, inertial sensor, motion control, pattern matching