Mobile Health pp 791-812 | Cite as

Motion Capture: From Radio Signals to Inertial Signals

  • Matteo Giuberti
  • Gianluigi Ferrari
Part of the Springer Series in Bio-/Neuroinformatics book series (SSBN, volume 5)


The study of the motion of individuals allows to gather relevant information on a person status, to be used in several fields (e.g., medical, sport, and entertainment). Over the past decade, the research activity in motion capture has benefited from the progress of portable and mobile sensors, paving the way toward the use of motion capture techniques in mHealth applications (e.g., remote monitoring of patients, and telerehabilitation). Indeed, even if the optical motion capture, which typically relies on a set of fixed cameras and body-worn reflecting markers, is generally perceived as the standard reference approach, other motion capture techniques, such as radio and inertial, are attracting an increasing attention because of their suitability in remote mHealth applications.

Moreover, several hybrid approaches have been studied and proposed in order to overcome the limitations of component technologies considered independently. In this chapter, we present an overview of possible integration strategies between radio and inertial motion capture techniques. We start by investigating a radio-based approach, based on the fingerprinting radio localization technique. Then, the previous approach is improved by integrating inertial measurements: namely, accelerometers are used to provide an estimate of the nodes’ pitches. Finally, the radio signals are abandoned in favor of only inertialmeasurements (obtained through accelerometers, gyroscopes, and magnetometers). The advantages and limitations of all approaches are discussed in a comparative way, characterizing the similarities and differences between the various approaches.


Motion capture radio fingerprinting inertial sensors remote monitoring body area network body sensor network sensor fusion 


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  1. 1.
  2. 2.
    Kinect: markerless optical motion capture system from Microsoft,
  3. 3.
    Optoelectronic motion capture system from Vicon,
  4. 4.
    Bahl, P., Padmanabhan, V.N.: RADAR: an in-building RF-based user location and tracking system. In: Proc. IEEE Conf. on Computer Commun (INFOCOM), Tel Aviv, Israel, vol. 2, pp. 775–784 (2000)Google Scholar
  5. 5.
    Bonato, P.: Wearable sensors and systems. IEEE Eng. Med. Biol. Mag. 29(3), 25–36 (2010)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Brodie, M., Walmsley, A., Page, W.: Fusion motion capture: a prototype system using inertial measurement units and GPS for the biomechanical analysis of ski racing. Sports Technology 1(1), 17–28 (2008)CrossRefGoogle Scholar
  7. 7.
    Chen, B., Patel, S., Buckley, T., Rednic, R., McClure, D., Shih, L., Tarsy, D., Welsh, M., Bonato, P.: A web-based system for home monitoring of patients with Parkinson’s Disease using wearable sensors. IEEE Transactions on Biomedical Engineering 58(3), 831–836 (2011)CrossRefGoogle Scholar
  8. 8.
    Chen, L., Wei, H., Ferryman, J.: A survey of human motion analysis using depth imagery. Pattern Recognition Letters 34(15), 1995–2006 (2013)CrossRefGoogle Scholar
  9. 9.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification and Scene Analysis, 2nd edn. Wiley-Interscience, New York (2000)Google Scholar
  10. 10.
    Favre, J., Jolles, B.M., Siegrist, O., Aminian, K.: Quaternion-based fusion of gyroscopes and accelerometers to improve 3D angle measurement. Electronics Letters 42(11), 6125–6614 (2006)CrossRefGoogle Scholar
  11. 11.
    Giuberti, M., Martalò, M., Ferrari, G.: Fingerprinting-based wireless 3D localization for motion capture applications. In: Proc. 1st ACM MobiHoc Workshop on Pervasive Wireless Healthcare (MobileHealth), Paris, France, pp. 6.1–6.8 (2011)Google Scholar
  12. 12.
    Giuberti, M., Martalò, M., Ferrari, G.: A hybrid radio/acclerometric approach to arm posture recognition. Journal of Ambient Intelligence and Smart Environments (2014); Under revision. Available upon requestGoogle Scholar
  13. 13.
    Kaemarungsi, K.: Design of indoor positioning systems based on location fingerprinting technique. PhD thesis, University of Pittsburgh, Pittsburgh (2005),
  14. 14.
    Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. of the ASME—Journal of Basic Eng. 82(Series D), 35–45 (1960)CrossRefGoogle Scholar
  15. 15.
    Kim, J., Seol, Y., Lee, J.: Realtime performance animation using sparse 3D motion sensors. In: Kallmann, M., Bekris, K. (eds.) MIG 2012. LNCS, vol. 7660, pp. 31–42. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  16. 16.
    Kuipers, J.B.: Quaternions and Rotation Sequences: A Primer with Applications to Orbits, Aerospace and Virtual Reality. Princeton University Press, Princeton (1999)zbMATHGoogle Scholar
  17. 17.
    Liu, H., Wei, X., Chai, J., Ha, I., Rhee, T.: Realtime human motion control with a small number of inertial sensors. In: Proc. of Symposium on Interactive 3D Graphics and games (I3D), San Francisco, CA, USA, pp. 133–140 (2011)Google Scholar
  18. 18.
    Lo, G., Suresh, A.R., Stocco, L., Gonzalez-Valenzuela, S., Leung, V.C.M.: A wireless sensor system for motion analysis of Parkinson’s disease patients. In: 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), Seattle, WA, USA, pp. 372–375 (2011)Google Scholar
  19. 19.
    Luinge, H.J., Veltink, P.H., Baten, C.T.M.: Ambulatory measurement of arm orientation. Journal of Biomechanics 40(1), 78–85 (2007)CrossRefGoogle Scholar
  20. 20.
    Madgwick, S.O.H.: An efficient orientation filter for inertial and inertial/magnetic sensor arrays. Tech. rep., Department of Mechanical Engineering, University of Bristol, Bristol, UK (2010),
  21. 21.
    Martalò, M., Giuberti, M., Ferrari, G.: Experimental investigation of wireless sensor networks for fingerprinting-based posture recognition. In: Riunione Annuale 2011 del Gruppo Nazionale Telecomunicazioni e Teoria dell’Informazione (GTTI), Messina and Taormina, Italy (2011)Google Scholar
  22. 22.
    Moeslund, T.B., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding 104(2), 90–126 (2006)CrossRefGoogle Scholar
  23. 23.
    Patel, S., Park, H., Bonato, P., Chan, L., Rodgers, M.: A review of wearable sensors and systems with application in rehabilitation. Journal of NeuroEngineering and Rehabilitation 9(21), 1–17 (2012)Google Scholar
  24. 24.
    Rahimi, F., Duval, C., Jog, M., Bee, C., South, A., Jog, M., Edwards, R., Boissy, P.: Capturing whole-body mobility of patients with Parkinson disease using inertial motion sensors: expected challenges and rewards. In: Proc. of the 33rd Annual Int. Conf. of the IEEE Eng. In: Medicine and Biology Society (EMBS), Boston, MA, USA, pp. 5833–5838 (2011)Google Scholar
  25. 25.
    Rodriguez-Silva, D.A., Gil-Castineira, F., Gonzalez-Castano, F.J., Duro, R.J., Lopez-Pena, F., Vales-Alonso, J.: Human motion tracking and gait analysis: a brief review of current sensing systems and integration with intelligent environments. In: Proc. of IEEE Int. Conf. on Virtual Environments, Human-Computer Interfaces, and Measurement Systems (VECIMS), Instanbul, Turkey, pp. 166–171 (2008)Google Scholar
  26. 26.
    Roetenberg, D., Slycke, P.J., Veltink, P.H.: Ambulatory position and orientation tracking fusing magnetic and inertial sensing. IEEE Trans. on Biomedical Eng. 54(5), 883–890 (2007)CrossRefGoogle Scholar
  27. 27.
    Roetenberg, D., Luinge, H.J., Slycke, P.J.: Xsens MVN: Full 6DOF human motion tracking using miniature inertial sensors. Xsens Technologies B V (2009)Google Scholar
  28. 28.
    Sabatini, A.M.: Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing. IEEE Trans. on Biomedical Eng. 53(7), 1346–1356 (2006)CrossRefGoogle Scholar
  29. 29.
    Salarian, A.: Ambulatory monitoring of motor functions in patients with Parkinson’s disease using kinematic sensors. PhD thesis, École polytechnique fédérale de Lausanne, Lausanne, Switzerland (2006),
  30. 30.
    Shepard, D.: A two-dimensional interpolation function for irregularly spaced data. In: ACM National Conf. (1968)Google Scholar
  31. 31.
    Swangmuang, N., Krishnamurthy, P.: Location fingerprint analyses toward efficient indoor positioning. In: IEEE Int. Conf. Pervasive Comp. and Commun. (PERCOM), Hong Kong, China, pp. 100–109 (2008)Google Scholar
  32. 32.
    Tao, Y., Hu, H., Zhou, H.: Integration of vision and inertial sensors for 3D arm motion tracking in home-based rehabilitation. The International Journal of Robotics Research 26(6), 607–624 (2007)CrossRefGoogle Scholar
  33. 33.
    Tautges, J., Zinke, A., Krüger, B., Baumann, J., Weber, A., Helten, T., Müller, M., Seidel, H., Eberhardt, B.: Motion reconstruction using sparse accelerometer data. ACM Trans. Graph. 30(3), 18:1–18:12 (2011)Google Scholar
  34. 34.
    US Wireless Corporation. RadioCamera Wireless Caller-Location System,
  35. 35.
    Viani, F., Robol, F., Polo, A., Rocca, P., Oliveri, G., Massa, A.: Wireless architectures for heterogeneous sensing in smart home applications: Concepts and real implementation. Proceedings of the IEEE 101(11), 2381–2396 (2013)CrossRefGoogle Scholar
  36. 36.
    Vlasic, D., Adelsberger, R., Vannucci, G., Barnwell, J., Gross, M., Matusik, W., Popović, J.: Practical motion capture in everyday surroundings. ACM Trans on Graphics 26(3), 35.1–35.9 (2007)Google Scholar
  37. 37.
    Wong, W.Y., Wong, M.S., Lo, K.H.: Clinical applications of sensors for human posture and movement analysis: a review. Prosthetics and Orthotics International 31(1), 62–75 (2007)CrossRefGoogle Scholar
  38. 38.
    Ying, H., Schlösser, M., Schnitzer, A., Schäfer, T., Schläfke, M.E., Leonhardt, S., Schiek, M.: Distributed intelligent sensor network for the rehabilitation of Parkinson’s patients. IEEE Trans. on Information Technology in Biomedicine 15(2), 268–276 (2011)CrossRefGoogle Scholar
  39. 39.
    Young, A.D.: Wireless realtime motion tracking system using localised orientation estimation. PhD thesis, University of Edinburgh, Edinburgh, UK (2010),
  40. 40.
    Yun, X., Bachmann, E.R., McGhee, R.B.: A simplified quaternion-based algorithm for orientation estimation from earth gravity and magnetic field measurements. IEEE Trans. on Instrumentation and Measurement 57(3), 638–650 (2008)CrossRefGoogle Scholar
  41. 41.
    Zheng, H., Black, N.D., Harris, N.D.: Position-sensing technologies for movement analysis in stroke rehabilitation. Medical and Biological Engineering and Computing 43(4), 413–420 (2005)CrossRefGoogle Scholar
  42. 42.
    Zwartjes, D.G.M., Heida, T., van Vugt, J.P.P., Geelen, J.A.G., Veltink, P.H.: Ambulatory monitoring of activities and motor symptoms in Parkinson’s Disease. IEEE Trans. on Biomedical Eng. 57(11), 2778–2786 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Xsens Technologies B.V.EnschedeThe Netherlands
  2. 2.Department of Information EngineeringUniversity of ParmaParmaItaly

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