Research on Biomedical Engineering

, Volume 35, Issue 3–4, pp 265–270 | Cite as

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

  • Gabrielly M. Moreira
  • Luiz H. F. Giovanini
  • Marcos P. R. de Castro
  • Guilherme N. Nogueira
  • Tatiane C. Boumer
  • Elisangela F. ManffraEmail author
Technical Communication



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.


Signal processing Biomedical engineering Microsoft Kinect 



The authors thank the volunteers who agreed to participate in this study and Jaury Almeida for the contribution.

Funding information

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

Compliance with ethical standards

University Ethics Committee approved the study (approval number 2.993.126).

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Sociedade Brasileira de Engenharia Biomedica 2019

Authors and Affiliations

  1. 1.Graduate Program in Health TechnologyPontifícia Universidade Católica do ParanáCuritibaBrazil
  2. 2.Graduate Program in Computer SciencePontifícia Universidade Católica do ParanáCuritibaBrazil
  3. 3.Undegraduate Program on Digital Games of Pontifícia Universidade Católica do ParanáCuritibaBrazil

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