Inertial Motion Capture System with an Adaptive Control Algorithm

  • Michał PielkaEmail author
  • Paweł Janik
  • Małgorzata Aneta Janik
  • Zygmunt Wróbel
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 925)


Motion capture (MoCap) systems are increasingly used for rehabilitation and diagnostic purposes. However, dissemination of this type of solutions requires further development and reduction of prices, especially for high-precision systems. The paper presents a tested MoCap system architecture, which is based on independent, integrated sensor modules. Some of the cheapest Wi-Fi 2.4 GHz transceivers with a SoC chip were used to build individual modules. This concept made it possible to obtain one of the smallest MoCap modules with a Wi-Fi interface. In turn, the implemented embedded software with an adaptive radio transmission control algorithm allows for optimization of the sensor module operation. The algorithm presented in the paper reduces both the transmission in the sensor network (by about 26%) as well as the router processor load (by about 80%).


Data transmissions IMU motion capture Sensor network Sensor power efficiency 


  1. 1.
    Rafal, K., Mateusz, K.: Preliminary study on accuracy of step length measurement for CIE exoskeleton. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 577–581. IEEE (2016)Google Scholar
  2. 2.
    Galan, B., Barry, G., Jackson, D., Mhiripiri, D., Olivier, P., Rochester, L.: Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson’s disease. Gait Posture 39, 1062–1068 (2014)CrossRefGoogle Scholar
  3. 3.
    Kuo, M.C., Chiang, P.Y., Kuo, C.C.J.: Coding of motion capture data via temporal-domain sampling and spatial-domain vector quantization techniques. In: Advances in Multimedia Information Processing, PCM, pp. 84–99 (2010)CrossRefGoogle Scholar
  4. 4.
    Guo, L., Xiong, S.: Accuracy of base of support using an inertial sensor based motion capture system. Sensors 17(9), 2091 (2017)CrossRefGoogle Scholar
  5. 5.
    Mańkowski, T., Tomczyński, J., Kaczmarek, P.: CIE-DataGlove, a multi-IMU system for hand posture tracking. In: International Conference Automation ICA, Advances in Intelligent Systems and Computing, AISC, vol. 550, pp. 268–276 (2017)CrossRefGoogle Scholar
  6. 6.
    Pons-Moll, G., Baak, A., Helten, T., Muller, M., Seidel, H., Rosenhahn, B.: Multisensor-fusion for 3D full-body human motion capture. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 663–670. IEEE (2010)Google Scholar
  7. 7.
    Eke, C., Cain, S., Stirling, L.: Strategy quantification using body worn inertial sensors in a reactive agility task. J. Biomech. 64, 219–225 (2017)CrossRefGoogle Scholar
  8. 8.
    Karatsidis, A., Bellusci, G., Schepers, H., de Zee, M., Andersen, M., Veltink, P.: Estimation of ground reaction forces and moments during gait using only inertial motion capture. Sensors 17, 75 (2017)CrossRefGoogle Scholar
  9. 9.
    Gleadhill, S., Lee, J., James, D.: The development and validation of using inertial sensors to monitor postural change in resistance exercise. J. Biomech. 49, 1259–1263 (2016)CrossRefGoogle Scholar
  10. 10.
    Madgwick, S.: An efficient orientation filter for inertial and inertial/magnetic sensor arrays. Technical report; Report x-io, University of Bristol (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Michał Pielka
    • 1
    Email author
  • Paweł Janik
    • 1
  • Małgorzata Aneta Janik
    • 1
  • Zygmunt Wróbel
    • 1
  1. 1.Faculty of Computer Science and Material ScienceUniversity of Silesia in KatowiceSosnowiecPoland

Personalised recommendations