Abstract
The paper presents the system to track and visualize the human movement. The system is equipped with inertial measurement units, consisting triaxial accelerometers, gyroscopes and magnetometers. The estimates of the relative position and orientation in the proposed system are obtained using the data from the sensors and a biomechanical model. To estimate the orientation of the body segments we applied Madgewick algorithm. The biomechanical model applied in our studies is based on twists and exponential maps. As a proof-of-concept, we applied our approach to an upper limb to illustrate its ability to track human motion.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abdel-Malek, K., Arora, J.: Human Motion Simulation: Predictive Dynamics. Academic Press (2013)
Ahmadi, A., Rowlands, D., James, D.: Towards a wearable device for skill assessment and skill acquisition of a tennis player during the first serve. Sports Technol. 2(3–4), 129–136 (2009)
Au, A., Kirsch, R.: EMG-based prediction of shoulder and elbow kinematics in able-bodied and spinal cord injured individuals. IEEE Trans. Rehabil. Eng. 8(4), 471–480 (2000)
Bao, L., Intille, S.: Activity recognition from user-annotated acceleration data. In: Pervasive Computing, pp. 1–17. Springer (2004)
Craig, J.: Wprowadzenie do robotyki: mechanika i sterowanie, pp. 35–128. Wydawnictwa Naukowo-Techniczne (1995)
Denavit, J.: A kinematic notation for lower-pair mechanisms based on matrices. ASME J. Appl. Mech, 215–221 (1955)
Fougner, A., Scheme, E., Chan, A., Englehart, K., Stavdahl, O.: A multi-modal approach for hand motion classification using surface emg and accelerometers. In: Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, pp. 4247–4250. IEEE (2011)
Guraliuc, A., Barsocchi, P., Potortì, F., Nepa, P.: Limb movements classification using wearable wireless transceivers. IEEE Trans. Inf Technol. Biomed. 15(3), 474–480 (2011)
Jaitner, T., Gawin, W.: A mobile measure device for the analysis of highly dynamic movement techniques. Procedia Eng. 2(2), 3005–3010 (2010)
Khaleghi, B., Khamis, A., Karray, F.: Multisensor data fusion. In: Multisensor Data Fusion: From Algorithms and Architectural Design to Applications, p. 15 (2015)
Llinas, J., Hall, D., Liggins, M.: Handbook of Multisensor Data Fusion: Theory and Practice. CRC Press, Boca Raton (2009)
Madgwick, S.: An efficient orientation filter for inertial and inertial/magnetic sensor arrays. Report x-io and University of Bristol (UK) (2010)
Madgwick, S., Harrison, A., Vaidyanathan, R.: Estimation of IMU and MARG orientation using a gradient descent algorithm. In: 2011 IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 1–7. IEEE (2011)
Madgwick, S., Vaidyanathan, R., Harrison, A.: An efficient orientation filter for inertial measurement units (IMUS) and magnetic angular rate and gravity (MARG) sensor arrays. Technical report, Department of Mechanical Engineering, April 2010
Mahony, R., Hamel, T., Pflimlin, J.: Nonlinear complementary filters on the special orthogonal group. IEEE Trans. Autom. Control 53(5), 1203–1218 (2008)
Murray, R., Li, Z., Sastry, S., Sastry, S.: A Mathematical Introduction to Robotic Manipulation. CRC Press, Boca Raton (1994)
Parent, R.: Animacja komputerowa: algorytmy i techniki, pp. 40–71, 181–209, 459–505. Wydawnictwo Naukowe PWN (2012)
Roetenberg, D., Luinge, H., Slycke, P.: Xsens MVN: full 6DOF human motion tracking using miniature inertial sensors. Technical report, Xsens Motion Technologies BV (2009)
Roetenberg, D., Luinge, H., Baten, C., Veltink, P.: Compensation of magnetic disturbances improves inertial and magnetic sensing of human body segment orientation. IEEE Trans. Neural Syst. Rehabil. Eng. 13(3), 395–405 (2005)
Shaeffer, D.: Mems inertial sensors: a tutorial overview. IEEE Commun. Mag. 51(4), 100–109 (2013)
Shimmer: Shimmer, October 2015. http://www.shimmersensing.com
Tchoń, K., Mazur, A., Hossa, R., Duleba, I., Muszyński, R.: Manipulatory i roboty mobilne. Modele, planowanie ruchu, sterowanie, pp. 23–90. Akademicka Oficyna Wydawnicza PLJ (2000)
Xi, C.: Human motion analysis with wearable inertial sensors. Ph.D. thesis, University of Tennessee, Knoxville, August 2013
Zhang, Z., Wong, W.C., Wu, J.: Wearable sensors for 3D upper limb motion modeling and ubiquitous estimation. J. Control Theory Appl. 9(1), 10–17 (2011)
Zhou, H., Hu, H., Harris, N., Hammerton, J.: Applications of wearable inertial sensors in estimation of upper limb movements. Biomed. Signal Process. Control 1(1), 22–32 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Leśniczek, Ł., Brzostowski, K. (2019). Application of the Sensor Fusion for the System to Track Human Upper Limb Motion. In: Choroś, K., Kopel, M., Kukla, E., Siemiński, A. (eds) Multimedia and Network Information Systems. MISSI 2018. Advances in Intelligent Systems and Computing, vol 833. Springer, Cham. https://doi.org/10.1007/978-3-319-98678-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-98678-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98677-7
Online ISBN: 978-3-319-98678-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)