Filtering motion signals from Microsoft Kinect® in the context of stroke rehabilitation

Abstract

The Microsoft Kinect® sensor has been employed for developing serious games and for biomechanics analysis. Both applications, when combined in the context of motor rehabilitation, might provide relevant data for therapists. However, the reliability of clinical data obtained with Kinect® is affected by filtering parameters which should be chosen according to spectral characteristics of the signals. In this paper we aim at determining the spectral characteristics of kinematics data collected with Kinect® during a serious game and to suggest adequate filtering. The motor tasks of lateral trunk inclination, trunk rotation, and shoulder abduction performed with heading, ski, and goalkeeper games originated 45 time series derived from 5 healthy people and 87 time series of 4 people with stroke. Time series were analyzed using the Fourier analysis and empirical mode decomposition (EMD). A residual analysis was performed to determine the optimal cutoff frequencies of the fourth-order low-pass Butterworth filters. Fourier and EMD analyses evidenced that the highest spectral power for header and goalkeeper tasks is below 3 Hz and for skiing, it is below 0.8 Hz. The ideal cutoff frequencies were around 3 Hz and 5 Hz and differed between healthy and stroke groups. The range of motion was affected by the cutoff frequencies. The signals captured by Kinect® have the main spectral components at lower frequencies and should be filtered at cutoff frequencies below 6 Hz. We recommend including the determination the impact of signal processing on clinical indicators in the workflow when developing a serious game for rehabilitation.